Add files using upload-large-folder tool
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
- data/mmlu_pro/01-ai/yi-1.5-34b-chat/c9134421-80f0-42d8-982b-f0f40754dd85.json +590 -0
- data/mmlu_pro/01-ai/yi-1.5-6b-chat/4f29bf2d-a5f4-416f-8e90-f6c1a951bebc.json +590 -0
- data/mmlu_pro/01-ai/yi-1.5-9b-chat/443312f3-5866-48c6-9fcb-f591df683fa5.json +590 -0
- data/mmlu_pro/01-ai/yi-34b/c8bce2aa-e6b1-4dcf-a874-2b65a547a792.json +590 -0
- data/mmlu_pro/01-ai/yi-6b-chat/a4db3d05-bc5a-4b9c-95f8-f7c339ed8282.json +590 -0
- data/mmlu_pro/01-ai/yi-6b/eaa5ed91-b28a-41a4-aeaa-c3f188496e18.json +590 -0
- data/mmlu_pro/01-ai/yi-large/6d386bbf-1224-41f9-a6b6-86ea931ced12.json +590 -0
- data/mmlu_pro/01-ai/yi-lightning/4f15fe1a-baf2-46d7-a83b-8f75d03591c7.json +589 -0
- data/mmlu_pro/Gemini-3-Pro(11/gemini-3-pro-11-25/e5c93fdf-9028-4e42-956d-a2c34770ba4d.json +71 -0
- data/mmlu_pro/NewenAI/newenai-phi4-sft/dfc932df-3c64-42db-a134-74cea5fb624f.json +72 -0
- data/mmlu_pro/abacus-ai/llama3-smaug-8b/bbd6e3fc-269b-43de-8c07-f53d4cdd345a.json +590 -0
- data/mmlu_pro/google/gemini-1.5-flash-002/0d1d88b0-fa49-4463-a184-e4302ffa4571.json +589 -0
- data/mmlu_pro/google/gemini-1.5-flash/01bb7276-91e8-44eb-ba3a-c7df072f7a38.json +589 -0
- data/mmlu_pro/google/gemini-1.5-pro-002/b3ffcd12-3a74-4f3f-9acf-7ec11615a88d.json +589 -0
- data/mmlu_pro/google/gemini-1.5-pro/fd7ad2ae-dd54-4275-8db7-7da0110eb2be.json +589 -0
- data/mmlu_pro/google/gemini-2.0-flash-exp/b70dfa85-e4ee-4eaa-9d5b-7f58a6ac2413.json +589 -0
- data/mmlu_pro/google/gemini-2.0-flash-lite/841ad2b0-86e3-44e4-88c2-d010d6c9c52e.json +71 -0
- data/mmlu_pro/google/gemini-2.0-flash/1961f28e-d519-422d-8221-fd482b2f60f5.json +71 -0
- data/mmlu_pro/google/gemini-2.0-pro/34eecaad-e501-4b2b-9e9c-284804d1a581.json +71 -0
- data/mmlu_pro/google/gemini-2.5-pro-exp-03-25/976b7201-8e01-49f0-b9ab-884d61e16288.json +589 -0
- data/mmlu_pro/google/gemini-2.5-pro/4ac5ed20-3428-462d-b108-9d4bfb2fed2b.json +71 -0
- data/mmlu_pro/google/gemini-3.1-flash-lite-preview/9e560681-ebd4-4d96-92fa-ef8e8e6c64fe.json +71 -0
- data/mmlu_pro/google/gemini-3.1-pro/c03b9f04-fa5b-41e8-b58f-0bfe85be3c0f.json +589 -0
- data/mmlu_pro/google/gemma-2-27b-it/e8295867-8053-488f-9130-9eb99b97670e.json +590 -0
- data/mmlu_pro/google/gemma-2-2b-it/7461516d-9358-42db-9888-056fd84c525f.json +72 -0
- data/mmlu_pro/google/gemma-2-9b-it/c9fcb231-ddb3-4c0a-8d57-3a3782f0d752.json +590 -0
- data/mmlu_pro/google/gemma-2-9b/9c2c6d87-805f-47c0-9ddf-e0b81d0f69f8.json +590 -0
- data/mmlu_pro/google/gemma-2b/eb556f91-0cbd-4530-a7d1-d80e86d42588.json +590 -0
- data/mmlu_pro/google/gemma-3-12b-it/83198456-6a19-4574-abf8-0a0cc97287af.json +72 -0
- data/mmlu_pro/google/gemma-3-1b-it/e3c1d8e7-a98f-4fd4-ac24-3d0c7c3669a9.json +72 -0
- data/mmlu_pro/google/gemma-3-27b-it/6a53d694-91e0-40bd-a265-1f640756ae66.json +72 -0
- data/mmlu_pro/google/gemma-3-4b-it/e7c8cf64-4f7a-4a14-8517-00760409be7c.json +72 -0
- data/mmlu_pro/google/gemma-7b/1e21b40e-8ae9-4712-87ad-344e5423846b.json +590 -0
- data/mmlu_pro/ibm/granite-3.0-2b-base/47ec1fc0-0ac3-4d86-87f6-274667ef2a16.json +590 -0
- data/mmlu_pro/ibm/granite-3.0-8b-base/5e811a7a-5a2c-4484-9368-5d62dab1f13f.json +590 -0
- data/mmlu_pro/ibm/granite-3.1-1b-a400m-base/1f8d80d4-01a3-4818-9a8b-d00054dcab43.json +590 -0
- data/mmlu_pro/ibm/granite-3.1-1b-a400m-instruct/f3eeed4c-1d73-46f0-acac-eafd5fc9754f.json +590 -0
- data/mmlu_pro/ibm/granite-3.1-2b-base/ba525354-6d57-4b26-b777-e58271890ae6.json +590 -0
- data/mmlu_pro/ibm/granite-3.1-2b-instruct/7959f684-3532-439e-a9e0-5389dcaf795c.json +590 -0
- data/mmlu_pro/ibm/granite-3.1-3b-a800m-base/95d5b3bc-f7e5-4171-9be8-9656a902d92f.json +590 -0
- data/mmlu_pro/ibm/granite-3.1-3b-a800m-instruct/b186c499-e9e3-4fef-8bbf-3c43f7c71e28.json +590 -0
- data/mmlu_pro/ibm/granite-3.1-8b-base/6b91c3fc-4ec1-480c-9e78-99440cbfe97d.json +590 -0
- data/mmlu_pro/ibm/granite-3.1-8b-instruct/07107f2c-5b3e-4b5d-87ec-0acc108a68f9.json +590 -0
- data/mmlu_pro/microsoft/phi-3.5-mini-instruct/61ae9c04-3c9a-47e3-bcab-eea1268d14fe.json +590 -0
- data/mmlu_pro/microsoft/phi-4-mini/86cb1a4f-477f-4c08-8fb4-d0a6153984c3.json +72 -0
- data/mmlu_pro/microsoft/phi-4-reasoning-plus/7f6559ff-c8dd-4fe7-9cd4-c1cd34e65c8b.json +72 -0
- data/mmlu_pro/microsoft/phi-4-reasoning/a61f15ba-23b9-48d9-bf7c-c1911d765421.json +72 -0
- data/mmlu_pro/microsoft/phi-4/c6c805a9-7065-4811-a2ee-aa9b3e065e09.json +72 -0
- data/mmlu_pro/microsoft/phi3-medium-128k/cfba928a-8592-47f2-bc5d-bb97cd624352.json +590 -0
- data/mmlu_pro/microsoft/phi3-medium-4k/996cc51e-f338-4192-ac74-c3149c097648.json +590 -0
data/mmlu_pro/01-ai/yi-1.5-34b-chat/c9134421-80f0-42d8-982b-f0f40754dd85.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/01-ai_yi-1.5-34b-chat/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Yi-1.5-34B-Chat",
|
| 25 |
+
"id": "01-ai/yi-1.5-34b-chat",
|
| 26 |
+
"developer": "01-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Yi-1.5-34B-Chat",
|
| 29 |
+
"size_billions_parameters": "34.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.5229
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.7141
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.5843
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.4753
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.539
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.6457
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3437
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.5819
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.5276
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.3479
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.5618
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.4629
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.4935
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.6429
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.5162
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/01-ai/yi-1.5-6b-chat/4f29bf2d-a5f4-416f-8e90-f6c1a951bebc.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/01-ai_yi-1.5-6b-chat/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Yi-1.5-6B-Chat",
|
| 25 |
+
"id": "01-ai/yi-1.5-6b-chat",
|
| 26 |
+
"developer": "01-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Yi-1.5-6B-Chat",
|
| 29 |
+
"size_billions_parameters": "6.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.3823
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.5746
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.4766
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.3074
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.4366
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.5273
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.2683
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.3362
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.3176
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2198
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.4145
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.3327
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.3564
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.5013
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.382
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/01-ai/yi-1.5-9b-chat/443312f3-5866-48c6-9fcb-f591df683fa5.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/01-ai_yi-1.5-9b-chat/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Yi-1.5-9B-Chat",
|
| 25 |
+
"id": "01-ai/yi-1.5-9b-chat",
|
| 26 |
+
"developer": "01-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Yi-1.5-9B-Chat",
|
| 29 |
+
"size_billions_parameters": "9.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.4595
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.6667
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.5425
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.3949
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.5
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.6019
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3323
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.4352
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.4094
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2661
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.5248
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.4008
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.4142
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.594
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.4491
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/01-ai/yi-34b/c8bce2aa-e6b1-4dcf-a874-2b65a547a792.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/01-ai_yi-34b/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Yi-34B",
|
| 25 |
+
"id": "01-ai/yi-34b",
|
| 26 |
+
"developer": "01-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Yi-34B",
|
| 29 |
+
"size_billions_parameters": "34.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.4303
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.6527
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.4005
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.265
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.4366
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.5569
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3261
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.5379
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.5197
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.327
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.3175
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.477
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.3503
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.6253
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.5509
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/01-ai/yi-6b-chat/a4db3d05-bc5a-4b9c-95f8-f7c339ed8282.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/01-ai_yi-6b-chat/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Yi-6B-Chat",
|
| 25 |
+
"id": "01-ai/yi-6b-chat",
|
| 26 |
+
"developer": "01-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Yi-6B-Chat",
|
| 29 |
+
"size_billions_parameters": "6.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.2884
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.477
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.2826
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1661
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.2659
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.3969
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.1899
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.3521
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.315
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2162
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.2124
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.3367
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.2094
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.4912
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.3506
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/01-ai/yi-6b/eaa5ed91-b28a-41a4-aeaa-c3f188496e18.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/01-ai_yi-6b/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Yi-6B",
|
| 25 |
+
"id": "01-ai/yi-6b",
|
| 26 |
+
"developer": "01-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Yi-6B",
|
| 29 |
+
"size_billions_parameters": "6.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.2651
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.4226
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.2864
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1484
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.2732
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.3578
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.1796
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.3166
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.294
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.1953
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.1902
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.3186
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.1832
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.4286
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.3496
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/01-ai/yi-large/6d386bbf-1224-41f9-a6b6-86ea931ced12.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/01-ai_yi-large/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Yi-large",
|
| 25 |
+
"id": "01-ai/yi-large",
|
| 26 |
+
"developer": "01-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Yi-large",
|
| 29 |
+
"size_billions_parameters": "150.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.5809
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.6987
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.6413
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.6166
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.6341
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.6813
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.4541
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.6443
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.4961
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.3624
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.6481
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.5531
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.5704
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.5063
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.6472
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/01-ai/yi-lightning/4f15fe1a-baf2-46d7-a83b-8f75d03591c7.json
ADDED
|
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/01-ai_yi-lightning/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Yi-Lightning",
|
| 25 |
+
"id": "01-ai/yi-lightning",
|
| 26 |
+
"developer": "01-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Yi-Lightning",
|
| 29 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.6238
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 72 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 73 |
+
"source_data": {
|
| 74 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 75 |
+
"source_type": "hf_dataset",
|
| 76 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 77 |
+
"hf_split": "train",
|
| 78 |
+
"additional_details": {
|
| 79 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 80 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 81 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 83 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 84 |
+
"dataset_total_questions": "12000",
|
| 85 |
+
"prompt_style": "5-shot CoT"
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"metric_config": {
|
| 89 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 90 |
+
"metric_id": "mmlu_pro/biology",
|
| 91 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 92 |
+
"metric_kind": "accuracy",
|
| 93 |
+
"metric_unit": "proportion",
|
| 94 |
+
"lower_is_better": false,
|
| 95 |
+
"score_type": "continuous",
|
| 96 |
+
"min_score": 0.0,
|
| 97 |
+
"max_score": 1.0,
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"aggregation": "accuracy_over_subset",
|
| 100 |
+
"prompt_style": "5-shot CoT"
|
| 101 |
+
}
|
| 102 |
+
},
|
| 103 |
+
"score_details": {
|
| 104 |
+
"score": 0.7964
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 109 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 110 |
+
"source_data": {
|
| 111 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 112 |
+
"source_type": "hf_dataset",
|
| 113 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 114 |
+
"hf_split": "train",
|
| 115 |
+
"additional_details": {
|
| 116 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 117 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 118 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 120 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 121 |
+
"dataset_total_questions": "12000",
|
| 122 |
+
"prompt_style": "5-shot CoT"
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"metric_config": {
|
| 126 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 127 |
+
"metric_id": "mmlu_pro/business",
|
| 128 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 129 |
+
"metric_kind": "accuracy",
|
| 130 |
+
"metric_unit": "proportion",
|
| 131 |
+
"lower_is_better": false,
|
| 132 |
+
"score_type": "continuous",
|
| 133 |
+
"min_score": 0.0,
|
| 134 |
+
"max_score": 1.0,
|
| 135 |
+
"additional_details": {
|
| 136 |
+
"aggregation": "accuracy_over_subset",
|
| 137 |
+
"prompt_style": "5-shot CoT"
|
| 138 |
+
}
|
| 139 |
+
},
|
| 140 |
+
"score_details": {
|
| 141 |
+
"score": 0.6907
|
| 142 |
+
}
|
| 143 |
+
},
|
| 144 |
+
{
|
| 145 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 146 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 147 |
+
"source_data": {
|
| 148 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 151 |
+
"hf_split": "train",
|
| 152 |
+
"additional_details": {
|
| 153 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 154 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 155 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 157 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 158 |
+
"dataset_total_questions": "12000",
|
| 159 |
+
"prompt_style": "5-shot CoT"
|
| 160 |
+
}
|
| 161 |
+
},
|
| 162 |
+
"metric_config": {
|
| 163 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 164 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 165 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 166 |
+
"metric_kind": "accuracy",
|
| 167 |
+
"metric_unit": "proportion",
|
| 168 |
+
"lower_is_better": false,
|
| 169 |
+
"score_type": "continuous",
|
| 170 |
+
"min_score": 0.0,
|
| 171 |
+
"max_score": 1.0,
|
| 172 |
+
"additional_details": {
|
| 173 |
+
"aggregation": "accuracy_over_subset",
|
| 174 |
+
"prompt_style": "5-shot CoT"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"score_details": {
|
| 178 |
+
"score": 0.6193
|
| 179 |
+
}
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 183 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 184 |
+
"source_data": {
|
| 185 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 186 |
+
"source_type": "hf_dataset",
|
| 187 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 188 |
+
"hf_split": "train",
|
| 189 |
+
"additional_details": {
|
| 190 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 191 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 192 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 194 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 195 |
+
"dataset_total_questions": "12000",
|
| 196 |
+
"prompt_style": "5-shot CoT"
|
| 197 |
+
}
|
| 198 |
+
},
|
| 199 |
+
"metric_config": {
|
| 200 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 201 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 202 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 203 |
+
"metric_kind": "accuracy",
|
| 204 |
+
"metric_unit": "proportion",
|
| 205 |
+
"lower_is_better": false,
|
| 206 |
+
"score_type": "continuous",
|
| 207 |
+
"min_score": 0.0,
|
| 208 |
+
"max_score": 1.0,
|
| 209 |
+
"additional_details": {
|
| 210 |
+
"aggregation": "accuracy_over_subset",
|
| 211 |
+
"prompt_style": "5-shot CoT"
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"score_details": {
|
| 215 |
+
"score": 0.6439
|
| 216 |
+
}
|
| 217 |
+
},
|
| 218 |
+
{
|
| 219 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 220 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 221 |
+
"source_data": {
|
| 222 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 223 |
+
"source_type": "hf_dataset",
|
| 224 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 225 |
+
"hf_split": "train",
|
| 226 |
+
"additional_details": {
|
| 227 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 228 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 229 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 231 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 232 |
+
"dataset_total_questions": "12000",
|
| 233 |
+
"prompt_style": "5-shot CoT"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"metric_config": {
|
| 237 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 238 |
+
"metric_id": "mmlu_pro/economics",
|
| 239 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 240 |
+
"metric_kind": "accuracy",
|
| 241 |
+
"metric_unit": "proportion",
|
| 242 |
+
"lower_is_better": false,
|
| 243 |
+
"score_type": "continuous",
|
| 244 |
+
"min_score": 0.0,
|
| 245 |
+
"max_score": 1.0,
|
| 246 |
+
"additional_details": {
|
| 247 |
+
"aggregation": "accuracy_over_subset",
|
| 248 |
+
"prompt_style": "5-shot CoT"
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"score_details": {
|
| 252 |
+
"score": 0.731
|
| 253 |
+
}
|
| 254 |
+
},
|
| 255 |
+
{
|
| 256 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 257 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 258 |
+
"source_data": {
|
| 259 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 260 |
+
"source_type": "hf_dataset",
|
| 261 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 262 |
+
"hf_split": "train",
|
| 263 |
+
"additional_details": {
|
| 264 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 265 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 266 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 268 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 269 |
+
"dataset_total_questions": "12000",
|
| 270 |
+
"prompt_style": "5-shot CoT"
|
| 271 |
+
}
|
| 272 |
+
},
|
| 273 |
+
"metric_config": {
|
| 274 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 275 |
+
"metric_id": "mmlu_pro/engineering",
|
| 276 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 277 |
+
"metric_kind": "accuracy",
|
| 278 |
+
"metric_unit": "proportion",
|
| 279 |
+
"lower_is_better": false,
|
| 280 |
+
"score_type": "continuous",
|
| 281 |
+
"min_score": 0.0,
|
| 282 |
+
"max_score": 1.0,
|
| 283 |
+
"additional_details": {
|
| 284 |
+
"aggregation": "accuracy_over_subset",
|
| 285 |
+
"prompt_style": "5-shot CoT"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"score_details": {
|
| 289 |
+
"score": 0.4221
|
| 290 |
+
}
|
| 291 |
+
},
|
| 292 |
+
{
|
| 293 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 294 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 295 |
+
"source_data": {
|
| 296 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 297 |
+
"source_type": "hf_dataset",
|
| 298 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 299 |
+
"hf_split": "train",
|
| 300 |
+
"additional_details": {
|
| 301 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 302 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 303 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 305 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 306 |
+
"dataset_total_questions": "12000",
|
| 307 |
+
"prompt_style": "5-shot CoT"
|
| 308 |
+
}
|
| 309 |
+
},
|
| 310 |
+
"metric_config": {
|
| 311 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 312 |
+
"metric_id": "mmlu_pro/health",
|
| 313 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 314 |
+
"metric_kind": "accuracy",
|
| 315 |
+
"metric_unit": "proportion",
|
| 316 |
+
"lower_is_better": false,
|
| 317 |
+
"score_type": "continuous",
|
| 318 |
+
"min_score": 0.0,
|
| 319 |
+
"max_score": 1.0,
|
| 320 |
+
"additional_details": {
|
| 321 |
+
"aggregation": "accuracy_over_subset",
|
| 322 |
+
"prompt_style": "5-shot CoT"
|
| 323 |
+
}
|
| 324 |
+
},
|
| 325 |
+
"score_details": {
|
| 326 |
+
"score": 0.6553
|
| 327 |
+
}
|
| 328 |
+
},
|
| 329 |
+
{
|
| 330 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 331 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 332 |
+
"source_data": {
|
| 333 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 334 |
+
"source_type": "hf_dataset",
|
| 335 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 336 |
+
"hf_split": "train",
|
| 337 |
+
"additional_details": {
|
| 338 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 339 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 340 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 342 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 343 |
+
"dataset_total_questions": "12000",
|
| 344 |
+
"prompt_style": "5-shot CoT"
|
| 345 |
+
}
|
| 346 |
+
},
|
| 347 |
+
"metric_config": {
|
| 348 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 349 |
+
"metric_id": "mmlu_pro/history",
|
| 350 |
+
"metric_name": "MMLU-Pro (History)",
|
| 351 |
+
"metric_kind": "accuracy",
|
| 352 |
+
"metric_unit": "proportion",
|
| 353 |
+
"lower_is_better": false,
|
| 354 |
+
"score_type": "continuous",
|
| 355 |
+
"min_score": 0.0,
|
| 356 |
+
"max_score": 1.0,
|
| 357 |
+
"additional_details": {
|
| 358 |
+
"aggregation": "accuracy_over_subset",
|
| 359 |
+
"prompt_style": "5-shot CoT"
|
| 360 |
+
}
|
| 361 |
+
},
|
| 362 |
+
"score_details": {
|
| 363 |
+
"score": 0.5748
|
| 364 |
+
}
|
| 365 |
+
},
|
| 366 |
+
{
|
| 367 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 368 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 369 |
+
"source_data": {
|
| 370 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 371 |
+
"source_type": "hf_dataset",
|
| 372 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 373 |
+
"hf_split": "train",
|
| 374 |
+
"additional_details": {
|
| 375 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 376 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 377 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 379 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 380 |
+
"dataset_total_questions": "12000",
|
| 381 |
+
"prompt_style": "5-shot CoT"
|
| 382 |
+
}
|
| 383 |
+
},
|
| 384 |
+
"metric_config": {
|
| 385 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 386 |
+
"metric_id": "mmlu_pro/law",
|
| 387 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 388 |
+
"metric_kind": "accuracy",
|
| 389 |
+
"metric_unit": "proportion",
|
| 390 |
+
"lower_is_better": false,
|
| 391 |
+
"score_type": "continuous",
|
| 392 |
+
"min_score": 0.0,
|
| 393 |
+
"max_score": 1.0,
|
| 394 |
+
"additional_details": {
|
| 395 |
+
"aggregation": "accuracy_over_subset",
|
| 396 |
+
"prompt_style": "5-shot CoT"
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"score_details": {
|
| 400 |
+
"score": 0.3751
|
| 401 |
+
}
|
| 402 |
+
},
|
| 403 |
+
{
|
| 404 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 405 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 406 |
+
"source_data": {
|
| 407 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 408 |
+
"source_type": "hf_dataset",
|
| 409 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 410 |
+
"hf_split": "train",
|
| 411 |
+
"additional_details": {
|
| 412 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 413 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 414 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 416 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 417 |
+
"dataset_total_questions": "12000",
|
| 418 |
+
"prompt_style": "5-shot CoT"
|
| 419 |
+
}
|
| 420 |
+
},
|
| 421 |
+
"metric_config": {
|
| 422 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 423 |
+
"metric_id": "mmlu_pro/math",
|
| 424 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 425 |
+
"metric_kind": "accuracy",
|
| 426 |
+
"metric_unit": "proportion",
|
| 427 |
+
"lower_is_better": false,
|
| 428 |
+
"score_type": "continuous",
|
| 429 |
+
"min_score": 0.0,
|
| 430 |
+
"max_score": 1.0,
|
| 431 |
+
"additional_details": {
|
| 432 |
+
"aggregation": "accuracy_over_subset",
|
| 433 |
+
"prompt_style": "5-shot CoT"
|
| 434 |
+
}
|
| 435 |
+
},
|
| 436 |
+
"score_details": {
|
| 437 |
+
"score": 0.6913
|
| 438 |
+
}
|
| 439 |
+
},
|
| 440 |
+
{
|
| 441 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 442 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 443 |
+
"source_data": {
|
| 444 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 445 |
+
"source_type": "hf_dataset",
|
| 446 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 447 |
+
"hf_split": "train",
|
| 448 |
+
"additional_details": {
|
| 449 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 450 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 451 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 453 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 454 |
+
"dataset_total_questions": "12000",
|
| 455 |
+
"prompt_style": "5-shot CoT"
|
| 456 |
+
}
|
| 457 |
+
},
|
| 458 |
+
"metric_config": {
|
| 459 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 460 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 461 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 462 |
+
"metric_kind": "accuracy",
|
| 463 |
+
"metric_unit": "proportion",
|
| 464 |
+
"lower_is_better": false,
|
| 465 |
+
"score_type": "continuous",
|
| 466 |
+
"min_score": 0.0,
|
| 467 |
+
"max_score": 1.0,
|
| 468 |
+
"additional_details": {
|
| 469 |
+
"aggregation": "accuracy_over_subset",
|
| 470 |
+
"prompt_style": "5-shot CoT"
|
| 471 |
+
}
|
| 472 |
+
},
|
| 473 |
+
"score_details": {
|
| 474 |
+
"score": 0.5711
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 479 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 480 |
+
"source_data": {
|
| 481 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 482 |
+
"source_type": "hf_dataset",
|
| 483 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 484 |
+
"hf_split": "train",
|
| 485 |
+
"additional_details": {
|
| 486 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 487 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 488 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 490 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 491 |
+
"dataset_total_questions": "12000",
|
| 492 |
+
"prompt_style": "5-shot CoT"
|
| 493 |
+
}
|
| 494 |
+
},
|
| 495 |
+
"metric_config": {
|
| 496 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 497 |
+
"metric_id": "mmlu_pro/physics",
|
| 498 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 499 |
+
"metric_kind": "accuracy",
|
| 500 |
+
"metric_unit": "proportion",
|
| 501 |
+
"lower_is_better": false,
|
| 502 |
+
"score_type": "continuous",
|
| 503 |
+
"min_score": 0.0,
|
| 504 |
+
"max_score": 1.0,
|
| 505 |
+
"additional_details": {
|
| 506 |
+
"aggregation": "accuracy_over_subset",
|
| 507 |
+
"prompt_style": "5-shot CoT"
|
| 508 |
+
}
|
| 509 |
+
},
|
| 510 |
+
"score_details": {
|
| 511 |
+
"score": 0.6251
|
| 512 |
+
}
|
| 513 |
+
},
|
| 514 |
+
{
|
| 515 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 516 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 517 |
+
"source_data": {
|
| 518 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 519 |
+
"source_type": "hf_dataset",
|
| 520 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 521 |
+
"hf_split": "train",
|
| 522 |
+
"additional_details": {
|
| 523 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 524 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 525 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 527 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 528 |
+
"dataset_total_questions": "12000",
|
| 529 |
+
"prompt_style": "5-shot CoT"
|
| 530 |
+
}
|
| 531 |
+
},
|
| 532 |
+
"metric_config": {
|
| 533 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 534 |
+
"metric_id": "mmlu_pro/psychology",
|
| 535 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 536 |
+
"metric_kind": "accuracy",
|
| 537 |
+
"metric_unit": "proportion",
|
| 538 |
+
"lower_is_better": false,
|
| 539 |
+
"score_type": "continuous",
|
| 540 |
+
"min_score": 0.0,
|
| 541 |
+
"max_score": 1.0,
|
| 542 |
+
"additional_details": {
|
| 543 |
+
"aggregation": "accuracy_over_subset",
|
| 544 |
+
"prompt_style": "5-shot CoT"
|
| 545 |
+
}
|
| 546 |
+
},
|
| 547 |
+
"score_details": {
|
| 548 |
+
"score": 0.7293
|
| 549 |
+
}
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 553 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 554 |
+
"source_data": {
|
| 555 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 556 |
+
"source_type": "hf_dataset",
|
| 557 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 558 |
+
"hf_split": "train",
|
| 559 |
+
"additional_details": {
|
| 560 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 561 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 562 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 564 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 565 |
+
"dataset_total_questions": "12000",
|
| 566 |
+
"prompt_style": "5-shot CoT"
|
| 567 |
+
}
|
| 568 |
+
},
|
| 569 |
+
"metric_config": {
|
| 570 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 571 |
+
"metric_id": "mmlu_pro/other",
|
| 572 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 573 |
+
"metric_kind": "accuracy",
|
| 574 |
+
"metric_unit": "proportion",
|
| 575 |
+
"lower_is_better": false,
|
| 576 |
+
"score_type": "continuous",
|
| 577 |
+
"min_score": 0.0,
|
| 578 |
+
"max_score": 1.0,
|
| 579 |
+
"additional_details": {
|
| 580 |
+
"aggregation": "accuracy_over_subset",
|
| 581 |
+
"prompt_style": "5-shot CoT"
|
| 582 |
+
}
|
| 583 |
+
},
|
| 584 |
+
"score_details": {
|
| 585 |
+
"score": 0.6677
|
| 586 |
+
}
|
| 587 |
+
}
|
| 588 |
+
]
|
| 589 |
+
}
|
data/mmlu_pro/Gemini-3-Pro(11/gemini-3-pro-11-25/e5c93fdf-9028-4e42-956d-a2c34770ba4d.json
ADDED
|
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/Gemini-3-Pro(11_gemini-3-pro-11-25/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-3-Pro(11/25)",
|
| 25 |
+
"id": "Gemini-3-Pro(11/gemini-3-pro-11-25",
|
| 26 |
+
"developer": "Gemini-3-Pro(11",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-3-Pro(11/25)",
|
| 29 |
+
"leaderboard_data_source": "Self-Reported"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.901
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
]
|
| 71 |
+
}
|
data/mmlu_pro/NewenAI/newenai-phi4-sft/dfc932df-3c64-42db-a134-74cea5fb624f.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/NewenAI_newenai-phi4-sft/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "NewenAI/Phi4-sft",
|
| 25 |
+
"id": "NewenAI/newenai-phi4-sft",
|
| 26 |
+
"developer": "NewenAI",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "NewenAI/Phi4-sft",
|
| 29 |
+
"size_billions_parameters": "14.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.577
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/abacus-ai/llama3-smaug-8b/bbd6e3fc-269b-43de-8c07-f53d4cdd345a.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/abacus-ai_llama3-smaug-8b/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Llama3-Smaug-8B",
|
| 25 |
+
"id": "abacus-ai/llama3-smaug-8b",
|
| 26 |
+
"developer": "abacus-ai",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Llama3-Smaug-8B",
|
| 29 |
+
"size_billions_parameters": "8.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.3693
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.622
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.3738
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.2305
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.3658
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.4917
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.1981
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.4327
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.4199
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2652
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.3316
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.4502
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.3727
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.2856
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.5739
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/google/gemini-1.5-flash-002/0d1d88b0-fa49-4463-a184-e4302ffa4571.json
ADDED
|
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-1.5-flash-002/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-1.5-Flash-002",
|
| 25 |
+
"id": "google/gemini-1.5-flash-002",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-1.5-Flash-002",
|
| 29 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.6409
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 72 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 73 |
+
"source_data": {
|
| 74 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 75 |
+
"source_type": "hf_dataset",
|
| 76 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 77 |
+
"hf_split": "train",
|
| 78 |
+
"additional_details": {
|
| 79 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 80 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 81 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 83 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 84 |
+
"dataset_total_questions": "12000",
|
| 85 |
+
"prompt_style": "5-shot CoT"
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"metric_config": {
|
| 89 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 90 |
+
"metric_id": "mmlu_pro/biology",
|
| 91 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 92 |
+
"metric_kind": "accuracy",
|
| 93 |
+
"metric_unit": "proportion",
|
| 94 |
+
"lower_is_better": false,
|
| 95 |
+
"score_type": "continuous",
|
| 96 |
+
"min_score": 0.0,
|
| 97 |
+
"max_score": 1.0,
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"aggregation": "accuracy_over_subset",
|
| 100 |
+
"prompt_style": "5-shot CoT"
|
| 101 |
+
}
|
| 102 |
+
},
|
| 103 |
+
"score_details": {
|
| 104 |
+
"score": 0.8368
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 109 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 110 |
+
"source_data": {
|
| 111 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 112 |
+
"source_type": "hf_dataset",
|
| 113 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 114 |
+
"hf_split": "train",
|
| 115 |
+
"additional_details": {
|
| 116 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 117 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 118 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 120 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 121 |
+
"dataset_total_questions": "12000",
|
| 122 |
+
"prompt_style": "5-shot CoT"
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"metric_config": {
|
| 126 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 127 |
+
"metric_id": "mmlu_pro/business",
|
| 128 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 129 |
+
"metric_kind": "accuracy",
|
| 130 |
+
"metric_unit": "proportion",
|
| 131 |
+
"lower_is_better": false,
|
| 132 |
+
"score_type": "continuous",
|
| 133 |
+
"min_score": 0.0,
|
| 134 |
+
"max_score": 1.0,
|
| 135 |
+
"additional_details": {
|
| 136 |
+
"aggregation": "accuracy_over_subset",
|
| 137 |
+
"prompt_style": "5-shot CoT"
|
| 138 |
+
}
|
| 139 |
+
},
|
| 140 |
+
"score_details": {
|
| 141 |
+
"score": 0.7145
|
| 142 |
+
}
|
| 143 |
+
},
|
| 144 |
+
{
|
| 145 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 146 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 147 |
+
"source_data": {
|
| 148 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 151 |
+
"hf_split": "train",
|
| 152 |
+
"additional_details": {
|
| 153 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 154 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 155 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 157 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 158 |
+
"dataset_total_questions": "12000",
|
| 159 |
+
"prompt_style": "5-shot CoT"
|
| 160 |
+
}
|
| 161 |
+
},
|
| 162 |
+
"metric_config": {
|
| 163 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 164 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 165 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 166 |
+
"metric_kind": "accuracy",
|
| 167 |
+
"metric_unit": "proportion",
|
| 168 |
+
"lower_is_better": false,
|
| 169 |
+
"score_type": "continuous",
|
| 170 |
+
"min_score": 0.0,
|
| 171 |
+
"max_score": 1.0,
|
| 172 |
+
"additional_details": {
|
| 173 |
+
"aggregation": "accuracy_over_subset",
|
| 174 |
+
"prompt_style": "5-shot CoT"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"score_details": {
|
| 178 |
+
"score": 0.6708
|
| 179 |
+
}
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 183 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 184 |
+
"source_data": {
|
| 185 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 186 |
+
"source_type": "hf_dataset",
|
| 187 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 188 |
+
"hf_split": "train",
|
| 189 |
+
"additional_details": {
|
| 190 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 191 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 192 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 194 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 195 |
+
"dataset_total_questions": "12000",
|
| 196 |
+
"prompt_style": "5-shot CoT"
|
| 197 |
+
}
|
| 198 |
+
},
|
| 199 |
+
"metric_config": {
|
| 200 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 201 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 202 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 203 |
+
"metric_kind": "accuracy",
|
| 204 |
+
"metric_unit": "proportion",
|
| 205 |
+
"lower_is_better": false,
|
| 206 |
+
"score_type": "continuous",
|
| 207 |
+
"min_score": 0.0,
|
| 208 |
+
"max_score": 1.0,
|
| 209 |
+
"additional_details": {
|
| 210 |
+
"aggregation": "accuracy_over_subset",
|
| 211 |
+
"prompt_style": "5-shot CoT"
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"score_details": {
|
| 215 |
+
"score": 0.6341
|
| 216 |
+
}
|
| 217 |
+
},
|
| 218 |
+
{
|
| 219 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 220 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 221 |
+
"source_data": {
|
| 222 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 223 |
+
"source_type": "hf_dataset",
|
| 224 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 225 |
+
"hf_split": "train",
|
| 226 |
+
"additional_details": {
|
| 227 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 228 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 229 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 231 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 232 |
+
"dataset_total_questions": "12000",
|
| 233 |
+
"prompt_style": "5-shot CoT"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"metric_config": {
|
| 237 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 238 |
+
"metric_id": "mmlu_pro/economics",
|
| 239 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 240 |
+
"metric_kind": "accuracy",
|
| 241 |
+
"metric_unit": "proportion",
|
| 242 |
+
"lower_is_better": false,
|
| 243 |
+
"score_type": "continuous",
|
| 244 |
+
"min_score": 0.0,
|
| 245 |
+
"max_score": 1.0,
|
| 246 |
+
"additional_details": {
|
| 247 |
+
"aggregation": "accuracy_over_subset",
|
| 248 |
+
"prompt_style": "5-shot CoT"
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"score_details": {
|
| 252 |
+
"score": 0.7628
|
| 253 |
+
}
|
| 254 |
+
},
|
| 255 |
+
{
|
| 256 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 257 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 258 |
+
"source_data": {
|
| 259 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 260 |
+
"source_type": "hf_dataset",
|
| 261 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 262 |
+
"hf_split": "train",
|
| 263 |
+
"additional_details": {
|
| 264 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 265 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 266 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 268 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 269 |
+
"dataset_total_questions": "12000",
|
| 270 |
+
"prompt_style": "5-shot CoT"
|
| 271 |
+
}
|
| 272 |
+
},
|
| 273 |
+
"metric_config": {
|
| 274 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 275 |
+
"metric_id": "mmlu_pro/engineering",
|
| 276 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 277 |
+
"metric_kind": "accuracy",
|
| 278 |
+
"metric_unit": "proportion",
|
| 279 |
+
"lower_is_better": false,
|
| 280 |
+
"score_type": "continuous",
|
| 281 |
+
"min_score": 0.0,
|
| 282 |
+
"max_score": 1.0,
|
| 283 |
+
"additional_details": {
|
| 284 |
+
"aggregation": "accuracy_over_subset",
|
| 285 |
+
"prompt_style": "5-shot CoT"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"score_details": {
|
| 289 |
+
"score": 0.407
|
| 290 |
+
}
|
| 291 |
+
},
|
| 292 |
+
{
|
| 293 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 294 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 295 |
+
"source_data": {
|
| 296 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 297 |
+
"source_type": "hf_dataset",
|
| 298 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 299 |
+
"hf_split": "train",
|
| 300 |
+
"additional_details": {
|
| 301 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 302 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 303 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 305 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 306 |
+
"dataset_total_questions": "12000",
|
| 307 |
+
"prompt_style": "5-shot CoT"
|
| 308 |
+
}
|
| 309 |
+
},
|
| 310 |
+
"metric_config": {
|
| 311 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 312 |
+
"metric_id": "mmlu_pro/health",
|
| 313 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 314 |
+
"metric_kind": "accuracy",
|
| 315 |
+
"metric_unit": "proportion",
|
| 316 |
+
"lower_is_better": false,
|
| 317 |
+
"score_type": "continuous",
|
| 318 |
+
"min_score": 0.0,
|
| 319 |
+
"max_score": 1.0,
|
| 320 |
+
"additional_details": {
|
| 321 |
+
"aggregation": "accuracy_over_subset",
|
| 322 |
+
"prompt_style": "5-shot CoT"
|
| 323 |
+
}
|
| 324 |
+
},
|
| 325 |
+
"score_details": {
|
| 326 |
+
"score": 0.6284
|
| 327 |
+
}
|
| 328 |
+
},
|
| 329 |
+
{
|
| 330 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 331 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 332 |
+
"source_data": {
|
| 333 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 334 |
+
"source_type": "hf_dataset",
|
| 335 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 336 |
+
"hf_split": "train",
|
| 337 |
+
"additional_details": {
|
| 338 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 339 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 340 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 342 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 343 |
+
"dataset_total_questions": "12000",
|
| 344 |
+
"prompt_style": "5-shot CoT"
|
| 345 |
+
}
|
| 346 |
+
},
|
| 347 |
+
"metric_config": {
|
| 348 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 349 |
+
"metric_id": "mmlu_pro/history",
|
| 350 |
+
"metric_name": "MMLU-Pro (History)",
|
| 351 |
+
"metric_kind": "accuracy",
|
| 352 |
+
"metric_unit": "proportion",
|
| 353 |
+
"lower_is_better": false,
|
| 354 |
+
"score_type": "continuous",
|
| 355 |
+
"min_score": 0.0,
|
| 356 |
+
"max_score": 1.0,
|
| 357 |
+
"additional_details": {
|
| 358 |
+
"aggregation": "accuracy_over_subset",
|
| 359 |
+
"prompt_style": "5-shot CoT"
|
| 360 |
+
}
|
| 361 |
+
},
|
| 362 |
+
"score_details": {
|
| 363 |
+
"score": 0.5932
|
| 364 |
+
}
|
| 365 |
+
},
|
| 366 |
+
{
|
| 367 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 368 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 369 |
+
"source_data": {
|
| 370 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 371 |
+
"source_type": "hf_dataset",
|
| 372 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 373 |
+
"hf_split": "train",
|
| 374 |
+
"additional_details": {
|
| 375 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 376 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 377 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 379 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 380 |
+
"dataset_total_questions": "12000",
|
| 381 |
+
"prompt_style": "5-shot CoT"
|
| 382 |
+
}
|
| 383 |
+
},
|
| 384 |
+
"metric_config": {
|
| 385 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 386 |
+
"metric_id": "mmlu_pro/law",
|
| 387 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 388 |
+
"metric_kind": "accuracy",
|
| 389 |
+
"metric_unit": "proportion",
|
| 390 |
+
"lower_is_better": false,
|
| 391 |
+
"score_type": "continuous",
|
| 392 |
+
"min_score": 0.0,
|
| 393 |
+
"max_score": 1.0,
|
| 394 |
+
"additional_details": {
|
| 395 |
+
"aggregation": "accuracy_over_subset",
|
| 396 |
+
"prompt_style": "5-shot CoT"
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"score_details": {
|
| 400 |
+
"score": 0.4286
|
| 401 |
+
}
|
| 402 |
+
},
|
| 403 |
+
{
|
| 404 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 405 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 406 |
+
"source_data": {
|
| 407 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 408 |
+
"source_type": "hf_dataset",
|
| 409 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 410 |
+
"hf_split": "train",
|
| 411 |
+
"additional_details": {
|
| 412 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 413 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 414 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 416 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 417 |
+
"dataset_total_questions": "12000",
|
| 418 |
+
"prompt_style": "5-shot CoT"
|
| 419 |
+
}
|
| 420 |
+
},
|
| 421 |
+
"metric_config": {
|
| 422 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 423 |
+
"metric_id": "mmlu_pro/math",
|
| 424 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 425 |
+
"metric_kind": "accuracy",
|
| 426 |
+
"metric_unit": "proportion",
|
| 427 |
+
"lower_is_better": false,
|
| 428 |
+
"score_type": "continuous",
|
| 429 |
+
"min_score": 0.0,
|
| 430 |
+
"max_score": 1.0,
|
| 431 |
+
"additional_details": {
|
| 432 |
+
"aggregation": "accuracy_over_subset",
|
| 433 |
+
"prompt_style": "5-shot CoT"
|
| 434 |
+
}
|
| 435 |
+
},
|
| 436 |
+
"score_details": {
|
| 437 |
+
"score": 0.6255
|
| 438 |
+
}
|
| 439 |
+
},
|
| 440 |
+
{
|
| 441 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 442 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 443 |
+
"source_data": {
|
| 444 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 445 |
+
"source_type": "hf_dataset",
|
| 446 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 447 |
+
"hf_split": "train",
|
| 448 |
+
"additional_details": {
|
| 449 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 450 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 451 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 453 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 454 |
+
"dataset_total_questions": "12000",
|
| 455 |
+
"prompt_style": "5-shot CoT"
|
| 456 |
+
}
|
| 457 |
+
},
|
| 458 |
+
"metric_config": {
|
| 459 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 460 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 461 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 462 |
+
"metric_kind": "accuracy",
|
| 463 |
+
"metric_unit": "proportion",
|
| 464 |
+
"lower_is_better": false,
|
| 465 |
+
"score_type": "continuous",
|
| 466 |
+
"min_score": 0.0,
|
| 467 |
+
"max_score": 1.0,
|
| 468 |
+
"additional_details": {
|
| 469 |
+
"aggregation": "accuracy_over_subset",
|
| 470 |
+
"prompt_style": "5-shot CoT"
|
| 471 |
+
}
|
| 472 |
+
},
|
| 473 |
+
"score_details": {
|
| 474 |
+
"score": 0.6052
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 479 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 480 |
+
"source_data": {
|
| 481 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 482 |
+
"source_type": "hf_dataset",
|
| 483 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 484 |
+
"hf_split": "train",
|
| 485 |
+
"additional_details": {
|
| 486 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 487 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 488 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 490 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 491 |
+
"dataset_total_questions": "12000",
|
| 492 |
+
"prompt_style": "5-shot CoT"
|
| 493 |
+
}
|
| 494 |
+
},
|
| 495 |
+
"metric_config": {
|
| 496 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 497 |
+
"metric_id": "mmlu_pro/physics",
|
| 498 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 499 |
+
"metric_kind": "accuracy",
|
| 500 |
+
"metric_unit": "proportion",
|
| 501 |
+
"lower_is_better": false,
|
| 502 |
+
"score_type": "continuous",
|
| 503 |
+
"min_score": 0.0,
|
| 504 |
+
"max_score": 1.0,
|
| 505 |
+
"additional_details": {
|
| 506 |
+
"aggregation": "accuracy_over_subset",
|
| 507 |
+
"prompt_style": "5-shot CoT"
|
| 508 |
+
}
|
| 509 |
+
},
|
| 510 |
+
"score_details": {
|
| 511 |
+
"score": 0.7141
|
| 512 |
+
}
|
| 513 |
+
},
|
| 514 |
+
{
|
| 515 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 516 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 517 |
+
"source_data": {
|
| 518 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 519 |
+
"source_type": "hf_dataset",
|
| 520 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 521 |
+
"hf_split": "train",
|
| 522 |
+
"additional_details": {
|
| 523 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 524 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 525 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 527 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 528 |
+
"dataset_total_questions": "12000",
|
| 529 |
+
"prompt_style": "5-shot CoT"
|
| 530 |
+
}
|
| 531 |
+
},
|
| 532 |
+
"metric_config": {
|
| 533 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 534 |
+
"metric_id": "mmlu_pro/psychology",
|
| 535 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 536 |
+
"metric_kind": "accuracy",
|
| 537 |
+
"metric_unit": "proportion",
|
| 538 |
+
"lower_is_better": false,
|
| 539 |
+
"score_type": "continuous",
|
| 540 |
+
"min_score": 0.0,
|
| 541 |
+
"max_score": 1.0,
|
| 542 |
+
"additional_details": {
|
| 543 |
+
"aggregation": "accuracy_over_subset",
|
| 544 |
+
"prompt_style": "5-shot CoT"
|
| 545 |
+
}
|
| 546 |
+
},
|
| 547 |
+
"score_details": {
|
| 548 |
+
"score": 0.7623
|
| 549 |
+
}
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 553 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 554 |
+
"source_data": {
|
| 555 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 556 |
+
"source_type": "hf_dataset",
|
| 557 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 558 |
+
"hf_split": "train",
|
| 559 |
+
"additional_details": {
|
| 560 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 561 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 562 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 564 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 565 |
+
"dataset_total_questions": "12000",
|
| 566 |
+
"prompt_style": "5-shot CoT"
|
| 567 |
+
}
|
| 568 |
+
},
|
| 569 |
+
"metric_config": {
|
| 570 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 571 |
+
"metric_id": "mmlu_pro/other",
|
| 572 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 573 |
+
"metric_kind": "accuracy",
|
| 574 |
+
"metric_unit": "proportion",
|
| 575 |
+
"lower_is_better": false,
|
| 576 |
+
"score_type": "continuous",
|
| 577 |
+
"min_score": 0.0,
|
| 578 |
+
"max_score": 1.0,
|
| 579 |
+
"additional_details": {
|
| 580 |
+
"aggregation": "accuracy_over_subset",
|
| 581 |
+
"prompt_style": "5-shot CoT"
|
| 582 |
+
}
|
| 583 |
+
},
|
| 584 |
+
"score_details": {
|
| 585 |
+
"score": 0.6453
|
| 586 |
+
}
|
| 587 |
+
}
|
| 588 |
+
]
|
| 589 |
+
}
|
data/mmlu_pro/google/gemini-1.5-flash/01bb7276-91e8-44eb-ba3a-c7df072f7a38.json
ADDED
|
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-1.5-flash/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-1.5-Flash",
|
| 25 |
+
"id": "google/gemini-1.5-flash",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-1.5-Flash",
|
| 29 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.5912
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 72 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 73 |
+
"source_data": {
|
| 74 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 75 |
+
"source_type": "hf_dataset",
|
| 76 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 77 |
+
"hf_split": "train",
|
| 78 |
+
"additional_details": {
|
| 79 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 80 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 81 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 83 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 84 |
+
"dataset_total_questions": "12000",
|
| 85 |
+
"prompt_style": "5-shot CoT"
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"metric_config": {
|
| 89 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 90 |
+
"metric_id": "mmlu_pro/biology",
|
| 91 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 92 |
+
"metric_kind": "accuracy",
|
| 93 |
+
"metric_unit": "proportion",
|
| 94 |
+
"lower_is_better": false,
|
| 95 |
+
"score_type": "continuous",
|
| 96 |
+
"min_score": 0.0,
|
| 97 |
+
"max_score": 1.0,
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"aggregation": "accuracy_over_subset",
|
| 100 |
+
"prompt_style": "5-shot CoT"
|
| 101 |
+
}
|
| 102 |
+
},
|
| 103 |
+
"score_details": {
|
| 104 |
+
"score": 0.8131
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 109 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 110 |
+
"source_data": {
|
| 111 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 112 |
+
"source_type": "hf_dataset",
|
| 113 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 114 |
+
"hf_split": "train",
|
| 115 |
+
"additional_details": {
|
| 116 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 117 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 118 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 120 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 121 |
+
"dataset_total_questions": "12000",
|
| 122 |
+
"prompt_style": "5-shot CoT"
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"metric_config": {
|
| 126 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 127 |
+
"metric_id": "mmlu_pro/business",
|
| 128 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 129 |
+
"metric_kind": "accuracy",
|
| 130 |
+
"metric_unit": "proportion",
|
| 131 |
+
"lower_is_better": false,
|
| 132 |
+
"score_type": "continuous",
|
| 133 |
+
"min_score": 0.0,
|
| 134 |
+
"max_score": 1.0,
|
| 135 |
+
"additional_details": {
|
| 136 |
+
"aggregation": "accuracy_over_subset",
|
| 137 |
+
"prompt_style": "5-shot CoT"
|
| 138 |
+
}
|
| 139 |
+
},
|
| 140 |
+
"score_details": {
|
| 141 |
+
"score": 0.667
|
| 142 |
+
}
|
| 143 |
+
},
|
| 144 |
+
{
|
| 145 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 146 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 147 |
+
"source_data": {
|
| 148 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 151 |
+
"hf_split": "train",
|
| 152 |
+
"additional_details": {
|
| 153 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 154 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 155 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 157 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 158 |
+
"dataset_total_questions": "12000",
|
| 159 |
+
"prompt_style": "5-shot CoT"
|
| 160 |
+
}
|
| 161 |
+
},
|
| 162 |
+
"metric_config": {
|
| 163 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 164 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 165 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 166 |
+
"metric_kind": "accuracy",
|
| 167 |
+
"metric_unit": "proportion",
|
| 168 |
+
"lower_is_better": false,
|
| 169 |
+
"score_type": "continuous",
|
| 170 |
+
"min_score": 0.0,
|
| 171 |
+
"max_score": 1.0,
|
| 172 |
+
"additional_details": {
|
| 173 |
+
"aggregation": "accuracy_over_subset",
|
| 174 |
+
"prompt_style": "5-shot CoT"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"score_details": {
|
| 178 |
+
"score": 0.613
|
| 179 |
+
}
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 183 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 184 |
+
"source_data": {
|
| 185 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 186 |
+
"source_type": "hf_dataset",
|
| 187 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 188 |
+
"hf_split": "train",
|
| 189 |
+
"additional_details": {
|
| 190 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 191 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 192 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 194 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 195 |
+
"dataset_total_questions": "12000",
|
| 196 |
+
"prompt_style": "5-shot CoT"
|
| 197 |
+
}
|
| 198 |
+
},
|
| 199 |
+
"metric_config": {
|
| 200 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 201 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 202 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 203 |
+
"metric_kind": "accuracy",
|
| 204 |
+
"metric_unit": "proportion",
|
| 205 |
+
"lower_is_better": false,
|
| 206 |
+
"score_type": "continuous",
|
| 207 |
+
"min_score": 0.0,
|
| 208 |
+
"max_score": 1.0,
|
| 209 |
+
"additional_details": {
|
| 210 |
+
"aggregation": "accuracy_over_subset",
|
| 211 |
+
"prompt_style": "5-shot CoT"
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"score_details": {
|
| 215 |
+
"score": 0.5951
|
| 216 |
+
}
|
| 217 |
+
},
|
| 218 |
+
{
|
| 219 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 220 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 221 |
+
"source_data": {
|
| 222 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 223 |
+
"source_type": "hf_dataset",
|
| 224 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 225 |
+
"hf_split": "train",
|
| 226 |
+
"additional_details": {
|
| 227 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 228 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 229 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 231 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 232 |
+
"dataset_total_questions": "12000",
|
| 233 |
+
"prompt_style": "5-shot CoT"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"metric_config": {
|
| 237 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 238 |
+
"metric_id": "mmlu_pro/economics",
|
| 239 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 240 |
+
"metric_kind": "accuracy",
|
| 241 |
+
"metric_unit": "proportion",
|
| 242 |
+
"lower_is_better": false,
|
| 243 |
+
"score_type": "continuous",
|
| 244 |
+
"min_score": 0.0,
|
| 245 |
+
"max_score": 1.0,
|
| 246 |
+
"additional_details": {
|
| 247 |
+
"aggregation": "accuracy_over_subset",
|
| 248 |
+
"prompt_style": "5-shot CoT"
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"score_details": {
|
| 252 |
+
"score": 0.6943
|
| 253 |
+
}
|
| 254 |
+
},
|
| 255 |
+
{
|
| 256 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 257 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 258 |
+
"source_data": {
|
| 259 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 260 |
+
"source_type": "hf_dataset",
|
| 261 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 262 |
+
"hf_split": "train",
|
| 263 |
+
"additional_details": {
|
| 264 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 265 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 266 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 268 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 269 |
+
"dataset_total_questions": "12000",
|
| 270 |
+
"prompt_style": "5-shot CoT"
|
| 271 |
+
}
|
| 272 |
+
},
|
| 273 |
+
"metric_config": {
|
| 274 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 275 |
+
"metric_id": "mmlu_pro/engineering",
|
| 276 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 277 |
+
"metric_kind": "accuracy",
|
| 278 |
+
"metric_unit": "proportion",
|
| 279 |
+
"lower_is_better": false,
|
| 280 |
+
"score_type": "continuous",
|
| 281 |
+
"min_score": 0.0,
|
| 282 |
+
"max_score": 1.0,
|
| 283 |
+
"additional_details": {
|
| 284 |
+
"aggregation": "accuracy_over_subset",
|
| 285 |
+
"prompt_style": "5-shot CoT"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"score_details": {
|
| 289 |
+
"score": 0.4416
|
| 290 |
+
}
|
| 291 |
+
},
|
| 292 |
+
{
|
| 293 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 294 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 295 |
+
"source_data": {
|
| 296 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 297 |
+
"source_type": "hf_dataset",
|
| 298 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 299 |
+
"hf_split": "train",
|
| 300 |
+
"additional_details": {
|
| 301 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 302 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 303 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 305 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 306 |
+
"dataset_total_questions": "12000",
|
| 307 |
+
"prompt_style": "5-shot CoT"
|
| 308 |
+
}
|
| 309 |
+
},
|
| 310 |
+
"metric_config": {
|
| 311 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 312 |
+
"metric_id": "mmlu_pro/health",
|
| 313 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 314 |
+
"metric_kind": "accuracy",
|
| 315 |
+
"metric_unit": "proportion",
|
| 316 |
+
"lower_is_better": false,
|
| 317 |
+
"score_type": "continuous",
|
| 318 |
+
"min_score": 0.0,
|
| 319 |
+
"max_score": 1.0,
|
| 320 |
+
"additional_details": {
|
| 321 |
+
"aggregation": "accuracy_over_subset",
|
| 322 |
+
"prompt_style": "5-shot CoT"
|
| 323 |
+
}
|
| 324 |
+
},
|
| 325 |
+
"score_details": {
|
| 326 |
+
"score": 0.6039
|
| 327 |
+
}
|
| 328 |
+
},
|
| 329 |
+
{
|
| 330 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 331 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 332 |
+
"source_data": {
|
| 333 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 334 |
+
"source_type": "hf_dataset",
|
| 335 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 336 |
+
"hf_split": "train",
|
| 337 |
+
"additional_details": {
|
| 338 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 339 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 340 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 342 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 343 |
+
"dataset_total_questions": "12000",
|
| 344 |
+
"prompt_style": "5-shot CoT"
|
| 345 |
+
}
|
| 346 |
+
},
|
| 347 |
+
"metric_config": {
|
| 348 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 349 |
+
"metric_id": "mmlu_pro/history",
|
| 350 |
+
"metric_name": "MMLU-Pro (History)",
|
| 351 |
+
"metric_kind": "accuracy",
|
| 352 |
+
"metric_unit": "proportion",
|
| 353 |
+
"lower_is_better": false,
|
| 354 |
+
"score_type": "continuous",
|
| 355 |
+
"min_score": 0.0,
|
| 356 |
+
"max_score": 1.0,
|
| 357 |
+
"additional_details": {
|
| 358 |
+
"aggregation": "accuracy_over_subset",
|
| 359 |
+
"prompt_style": "5-shot CoT"
|
| 360 |
+
}
|
| 361 |
+
},
|
| 362 |
+
"score_details": {
|
| 363 |
+
"score": 0.538
|
| 364 |
+
}
|
| 365 |
+
},
|
| 366 |
+
{
|
| 367 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 368 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 369 |
+
"source_data": {
|
| 370 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 371 |
+
"source_type": "hf_dataset",
|
| 372 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 373 |
+
"hf_split": "train",
|
| 374 |
+
"additional_details": {
|
| 375 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 376 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 377 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 379 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 380 |
+
"dataset_total_questions": "12000",
|
| 381 |
+
"prompt_style": "5-shot CoT"
|
| 382 |
+
}
|
| 383 |
+
},
|
| 384 |
+
"metric_config": {
|
| 385 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 386 |
+
"metric_id": "mmlu_pro/law",
|
| 387 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 388 |
+
"metric_kind": "accuracy",
|
| 389 |
+
"metric_unit": "proportion",
|
| 390 |
+
"lower_is_better": false,
|
| 391 |
+
"score_type": "continuous",
|
| 392 |
+
"min_score": 0.0,
|
| 393 |
+
"max_score": 1.0,
|
| 394 |
+
"additional_details": {
|
| 395 |
+
"aggregation": "accuracy_over_subset",
|
| 396 |
+
"prompt_style": "5-shot CoT"
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"score_details": {
|
| 400 |
+
"score": 0.3732
|
| 401 |
+
}
|
| 402 |
+
},
|
| 403 |
+
{
|
| 404 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 405 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 406 |
+
"source_data": {
|
| 407 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 408 |
+
"source_type": "hf_dataset",
|
| 409 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 410 |
+
"hf_split": "train",
|
| 411 |
+
"additional_details": {
|
| 412 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 413 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 414 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 416 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 417 |
+
"dataset_total_questions": "12000",
|
| 418 |
+
"prompt_style": "5-shot CoT"
|
| 419 |
+
}
|
| 420 |
+
},
|
| 421 |
+
"metric_config": {
|
| 422 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 423 |
+
"metric_id": "mmlu_pro/math",
|
| 424 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 425 |
+
"metric_kind": "accuracy",
|
| 426 |
+
"metric_unit": "proportion",
|
| 427 |
+
"lower_is_better": false,
|
| 428 |
+
"score_type": "continuous",
|
| 429 |
+
"min_score": 0.0,
|
| 430 |
+
"max_score": 1.0,
|
| 431 |
+
"additional_details": {
|
| 432 |
+
"aggregation": "accuracy_over_subset",
|
| 433 |
+
"prompt_style": "5-shot CoT"
|
| 434 |
+
}
|
| 435 |
+
},
|
| 436 |
+
"score_details": {
|
| 437 |
+
"score": 0.5958
|
| 438 |
+
}
|
| 439 |
+
},
|
| 440 |
+
{
|
| 441 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 442 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 443 |
+
"source_data": {
|
| 444 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 445 |
+
"source_type": "hf_dataset",
|
| 446 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 447 |
+
"hf_split": "train",
|
| 448 |
+
"additional_details": {
|
| 449 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 450 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 451 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 453 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 454 |
+
"dataset_total_questions": "12000",
|
| 455 |
+
"prompt_style": "5-shot CoT"
|
| 456 |
+
}
|
| 457 |
+
},
|
| 458 |
+
"metric_config": {
|
| 459 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 460 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 461 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 462 |
+
"metric_kind": "accuracy",
|
| 463 |
+
"metric_unit": "proportion",
|
| 464 |
+
"lower_is_better": false,
|
| 465 |
+
"score_type": "continuous",
|
| 466 |
+
"min_score": 0.0,
|
| 467 |
+
"max_score": 1.0,
|
| 468 |
+
"additional_details": {
|
| 469 |
+
"aggregation": "accuracy_over_subset",
|
| 470 |
+
"prompt_style": "5-shot CoT"
|
| 471 |
+
}
|
| 472 |
+
},
|
| 473 |
+
"score_details": {
|
| 474 |
+
"score": 0.4949
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 479 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 480 |
+
"source_data": {
|
| 481 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 482 |
+
"source_type": "hf_dataset",
|
| 483 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 484 |
+
"hf_split": "train",
|
| 485 |
+
"additional_details": {
|
| 486 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 487 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 488 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 490 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 491 |
+
"dataset_total_questions": "12000",
|
| 492 |
+
"prompt_style": "5-shot CoT"
|
| 493 |
+
}
|
| 494 |
+
},
|
| 495 |
+
"metric_config": {
|
| 496 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 497 |
+
"metric_id": "mmlu_pro/physics",
|
| 498 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 499 |
+
"metric_kind": "accuracy",
|
| 500 |
+
"metric_unit": "proportion",
|
| 501 |
+
"lower_is_better": false,
|
| 502 |
+
"score_type": "continuous",
|
| 503 |
+
"min_score": 0.0,
|
| 504 |
+
"max_score": 1.0,
|
| 505 |
+
"additional_details": {
|
| 506 |
+
"aggregation": "accuracy_over_subset",
|
| 507 |
+
"prompt_style": "5-shot CoT"
|
| 508 |
+
}
|
| 509 |
+
},
|
| 510 |
+
"score_details": {
|
| 511 |
+
"score": 0.612
|
| 512 |
+
}
|
| 513 |
+
},
|
| 514 |
+
{
|
| 515 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 516 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 517 |
+
"source_data": {
|
| 518 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 519 |
+
"source_type": "hf_dataset",
|
| 520 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 521 |
+
"hf_split": "train",
|
| 522 |
+
"additional_details": {
|
| 523 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 524 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 525 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 527 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 528 |
+
"dataset_total_questions": "12000",
|
| 529 |
+
"prompt_style": "5-shot CoT"
|
| 530 |
+
}
|
| 531 |
+
},
|
| 532 |
+
"metric_config": {
|
| 533 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 534 |
+
"metric_id": "mmlu_pro/psychology",
|
| 535 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 536 |
+
"metric_kind": "accuracy",
|
| 537 |
+
"metric_unit": "proportion",
|
| 538 |
+
"lower_is_better": false,
|
| 539 |
+
"score_type": "continuous",
|
| 540 |
+
"min_score": 0.0,
|
| 541 |
+
"max_score": 1.0,
|
| 542 |
+
"additional_details": {
|
| 543 |
+
"aggregation": "accuracy_over_subset",
|
| 544 |
+
"prompt_style": "5-shot CoT"
|
| 545 |
+
}
|
| 546 |
+
},
|
| 547 |
+
"score_details": {
|
| 548 |
+
"score": 0.7005
|
| 549 |
+
}
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 553 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 554 |
+
"source_data": {
|
| 555 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 556 |
+
"source_type": "hf_dataset",
|
| 557 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 558 |
+
"hf_split": "train",
|
| 559 |
+
"additional_details": {
|
| 560 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 561 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 562 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 564 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 565 |
+
"dataset_total_questions": "12000",
|
| 566 |
+
"prompt_style": "5-shot CoT"
|
| 567 |
+
}
|
| 568 |
+
},
|
| 569 |
+
"metric_config": {
|
| 570 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 571 |
+
"metric_id": "mmlu_pro/other",
|
| 572 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 573 |
+
"metric_kind": "accuracy",
|
| 574 |
+
"metric_unit": "proportion",
|
| 575 |
+
"lower_is_better": false,
|
| 576 |
+
"score_type": "continuous",
|
| 577 |
+
"min_score": 0.0,
|
| 578 |
+
"max_score": 1.0,
|
| 579 |
+
"additional_details": {
|
| 580 |
+
"aggregation": "accuracy_over_subset",
|
| 581 |
+
"prompt_style": "5-shot CoT"
|
| 582 |
+
}
|
| 583 |
+
},
|
| 584 |
+
"score_details": {
|
| 585 |
+
"score": 0.58
|
| 586 |
+
}
|
| 587 |
+
}
|
| 588 |
+
]
|
| 589 |
+
}
|
data/mmlu_pro/google/gemini-1.5-pro-002/b3ffcd12-3a74-4f3f-9acf-7ec11615a88d.json
ADDED
|
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-1.5-pro-002/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-1.5-Pro-002",
|
| 25 |
+
"id": "google/gemini-1.5-pro-002",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-1.5-Pro-002",
|
| 29 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.7025
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 72 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 73 |
+
"source_data": {
|
| 74 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 75 |
+
"source_type": "hf_dataset",
|
| 76 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 77 |
+
"hf_split": "train",
|
| 78 |
+
"additional_details": {
|
| 79 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 80 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 81 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 83 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 84 |
+
"dataset_total_questions": "12000",
|
| 85 |
+
"prompt_style": "5-shot CoT"
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"metric_config": {
|
| 89 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 90 |
+
"metric_id": "mmlu_pro/biology",
|
| 91 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 92 |
+
"metric_kind": "accuracy",
|
| 93 |
+
"metric_unit": "proportion",
|
| 94 |
+
"lower_is_better": false,
|
| 95 |
+
"score_type": "continuous",
|
| 96 |
+
"min_score": 0.0,
|
| 97 |
+
"max_score": 1.0,
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"aggregation": "accuracy_over_subset",
|
| 100 |
+
"prompt_style": "5-shot CoT"
|
| 101 |
+
}
|
| 102 |
+
},
|
| 103 |
+
"score_details": {
|
| 104 |
+
"score": 0.8645
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 109 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 110 |
+
"source_data": {
|
| 111 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 112 |
+
"source_type": "hf_dataset",
|
| 113 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 114 |
+
"hf_split": "train",
|
| 115 |
+
"additional_details": {
|
| 116 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 117 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 118 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 120 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 121 |
+
"dataset_total_questions": "12000",
|
| 122 |
+
"prompt_style": "5-shot CoT"
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"metric_config": {
|
| 126 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 127 |
+
"metric_id": "mmlu_pro/business",
|
| 128 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 129 |
+
"metric_kind": "accuracy",
|
| 130 |
+
"metric_unit": "proportion",
|
| 131 |
+
"lower_is_better": false,
|
| 132 |
+
"score_type": "continuous",
|
| 133 |
+
"min_score": 0.0,
|
| 134 |
+
"max_score": 1.0,
|
| 135 |
+
"additional_details": {
|
| 136 |
+
"aggregation": "accuracy_over_subset",
|
| 137 |
+
"prompt_style": "5-shot CoT"
|
| 138 |
+
}
|
| 139 |
+
},
|
| 140 |
+
"score_details": {
|
| 141 |
+
"score": 0.8094
|
| 142 |
+
}
|
| 143 |
+
},
|
| 144 |
+
{
|
| 145 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 146 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 147 |
+
"source_data": {
|
| 148 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 151 |
+
"hf_split": "train",
|
| 152 |
+
"additional_details": {
|
| 153 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 154 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 155 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 157 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 158 |
+
"dataset_total_questions": "12000",
|
| 159 |
+
"prompt_style": "5-shot CoT"
|
| 160 |
+
}
|
| 161 |
+
},
|
| 162 |
+
"metric_config": {
|
| 163 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 164 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 165 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 166 |
+
"metric_kind": "accuracy",
|
| 167 |
+
"metric_unit": "proportion",
|
| 168 |
+
"lower_is_better": false,
|
| 169 |
+
"score_type": "continuous",
|
| 170 |
+
"min_score": 0.0,
|
| 171 |
+
"max_score": 1.0,
|
| 172 |
+
"additional_details": {
|
| 173 |
+
"aggregation": "accuracy_over_subset",
|
| 174 |
+
"prompt_style": "5-shot CoT"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"score_details": {
|
| 178 |
+
"score": 0.6221
|
| 179 |
+
}
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 183 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 184 |
+
"source_data": {
|
| 185 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 186 |
+
"source_type": "hf_dataset",
|
| 187 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 188 |
+
"hf_split": "train",
|
| 189 |
+
"additional_details": {
|
| 190 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 191 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 192 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 194 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 195 |
+
"dataset_total_questions": "12000",
|
| 196 |
+
"prompt_style": "5-shot CoT"
|
| 197 |
+
}
|
| 198 |
+
},
|
| 199 |
+
"metric_config": {
|
| 200 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 201 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 202 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 203 |
+
"metric_kind": "accuracy",
|
| 204 |
+
"metric_unit": "proportion",
|
| 205 |
+
"lower_is_better": false,
|
| 206 |
+
"score_type": "continuous",
|
| 207 |
+
"min_score": 0.0,
|
| 208 |
+
"max_score": 1.0,
|
| 209 |
+
"additional_details": {
|
| 210 |
+
"aggregation": "accuracy_over_subset",
|
| 211 |
+
"prompt_style": "5-shot CoT"
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"score_details": {
|
| 215 |
+
"score": 0.7122
|
| 216 |
+
}
|
| 217 |
+
},
|
| 218 |
+
{
|
| 219 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 220 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 221 |
+
"source_data": {
|
| 222 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 223 |
+
"source_type": "hf_dataset",
|
| 224 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 225 |
+
"hf_split": "train",
|
| 226 |
+
"additional_details": {
|
| 227 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 228 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 229 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 231 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 232 |
+
"dataset_total_questions": "12000",
|
| 233 |
+
"prompt_style": "5-shot CoT"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"metric_config": {
|
| 237 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 238 |
+
"metric_id": "mmlu_pro/economics",
|
| 239 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 240 |
+
"metric_kind": "accuracy",
|
| 241 |
+
"metric_unit": "proportion",
|
| 242 |
+
"lower_is_better": false,
|
| 243 |
+
"score_type": "continuous",
|
| 244 |
+
"min_score": 0.0,
|
| 245 |
+
"max_score": 1.0,
|
| 246 |
+
"additional_details": {
|
| 247 |
+
"aggregation": "accuracy_over_subset",
|
| 248 |
+
"prompt_style": "5-shot CoT"
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"score_details": {
|
| 252 |
+
"score": 0.8171
|
| 253 |
+
}
|
| 254 |
+
},
|
| 255 |
+
{
|
| 256 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 257 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 258 |
+
"source_data": {
|
| 259 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 260 |
+
"source_type": "hf_dataset",
|
| 261 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 262 |
+
"hf_split": "train",
|
| 263 |
+
"additional_details": {
|
| 264 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 265 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 266 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 268 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 269 |
+
"dataset_total_questions": "12000",
|
| 270 |
+
"prompt_style": "5-shot CoT"
|
| 271 |
+
}
|
| 272 |
+
},
|
| 273 |
+
"metric_config": {
|
| 274 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 275 |
+
"metric_id": "mmlu_pro/engineering",
|
| 276 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 277 |
+
"metric_kind": "accuracy",
|
| 278 |
+
"metric_unit": "proportion",
|
| 279 |
+
"lower_is_better": false,
|
| 280 |
+
"score_type": "continuous",
|
| 281 |
+
"min_score": 0.0,
|
| 282 |
+
"max_score": 1.0,
|
| 283 |
+
"additional_details": {
|
| 284 |
+
"aggregation": "accuracy_over_subset",
|
| 285 |
+
"prompt_style": "5-shot CoT"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"score_details": {
|
| 289 |
+
"score": 0.5899
|
| 290 |
+
}
|
| 291 |
+
},
|
| 292 |
+
{
|
| 293 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 294 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 295 |
+
"source_data": {
|
| 296 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 297 |
+
"source_type": "hf_dataset",
|
| 298 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 299 |
+
"hf_split": "train",
|
| 300 |
+
"additional_details": {
|
| 301 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 302 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 303 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 305 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 306 |
+
"dataset_total_questions": "12000",
|
| 307 |
+
"prompt_style": "5-shot CoT"
|
| 308 |
+
}
|
| 309 |
+
},
|
| 310 |
+
"metric_config": {
|
| 311 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 312 |
+
"metric_id": "mmlu_pro/health",
|
| 313 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 314 |
+
"metric_kind": "accuracy",
|
| 315 |
+
"metric_unit": "proportion",
|
| 316 |
+
"lower_is_better": false,
|
| 317 |
+
"score_type": "continuous",
|
| 318 |
+
"min_score": 0.0,
|
| 319 |
+
"max_score": 1.0,
|
| 320 |
+
"additional_details": {
|
| 321 |
+
"aggregation": "accuracy_over_subset",
|
| 322 |
+
"prompt_style": "5-shot CoT"
|
| 323 |
+
}
|
| 324 |
+
},
|
| 325 |
+
"score_details": {
|
| 326 |
+
"score": 0.7479
|
| 327 |
+
}
|
| 328 |
+
},
|
| 329 |
+
{
|
| 330 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 331 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 332 |
+
"source_data": {
|
| 333 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 334 |
+
"source_type": "hf_dataset",
|
| 335 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 336 |
+
"hf_split": "train",
|
| 337 |
+
"additional_details": {
|
| 338 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 339 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 340 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 342 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 343 |
+
"dataset_total_questions": "12000",
|
| 344 |
+
"prompt_style": "5-shot CoT"
|
| 345 |
+
}
|
| 346 |
+
},
|
| 347 |
+
"metric_config": {
|
| 348 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 349 |
+
"metric_id": "mmlu_pro/history",
|
| 350 |
+
"metric_name": "MMLU-Pro (History)",
|
| 351 |
+
"metric_kind": "accuracy",
|
| 352 |
+
"metric_unit": "proportion",
|
| 353 |
+
"lower_is_better": false,
|
| 354 |
+
"score_type": "continuous",
|
| 355 |
+
"min_score": 0.0,
|
| 356 |
+
"max_score": 1.0,
|
| 357 |
+
"additional_details": {
|
| 358 |
+
"aggregation": "accuracy_over_subset",
|
| 359 |
+
"prompt_style": "5-shot CoT"
|
| 360 |
+
}
|
| 361 |
+
},
|
| 362 |
+
"score_details": {
|
| 363 |
+
"score": 0.7008
|
| 364 |
+
}
|
| 365 |
+
},
|
| 366 |
+
{
|
| 367 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 368 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 369 |
+
"source_data": {
|
| 370 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 371 |
+
"source_type": "hf_dataset",
|
| 372 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 373 |
+
"hf_split": "train",
|
| 374 |
+
"additional_details": {
|
| 375 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 376 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 377 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 379 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 380 |
+
"dataset_total_questions": "12000",
|
| 381 |
+
"prompt_style": "5-shot CoT"
|
| 382 |
+
}
|
| 383 |
+
},
|
| 384 |
+
"metric_config": {
|
| 385 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 386 |
+
"metric_id": "mmlu_pro/law",
|
| 387 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 388 |
+
"metric_kind": "accuracy",
|
| 389 |
+
"metric_unit": "proportion",
|
| 390 |
+
"lower_is_better": false,
|
| 391 |
+
"score_type": "continuous",
|
| 392 |
+
"min_score": 0.0,
|
| 393 |
+
"max_score": 1.0,
|
| 394 |
+
"additional_details": {
|
| 395 |
+
"aggregation": "accuracy_over_subset",
|
| 396 |
+
"prompt_style": "5-shot CoT"
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"score_details": {
|
| 400 |
+
"score": 0.5522
|
| 401 |
+
}
|
| 402 |
+
},
|
| 403 |
+
{
|
| 404 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 405 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 406 |
+
"source_data": {
|
| 407 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 408 |
+
"source_type": "hf_dataset",
|
| 409 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 410 |
+
"hf_split": "train",
|
| 411 |
+
"additional_details": {
|
| 412 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 413 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 414 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 416 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 417 |
+
"dataset_total_questions": "12000",
|
| 418 |
+
"prompt_style": "5-shot CoT"
|
| 419 |
+
}
|
| 420 |
+
},
|
| 421 |
+
"metric_config": {
|
| 422 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 423 |
+
"metric_id": "mmlu_pro/math",
|
| 424 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 425 |
+
"metric_kind": "accuracy",
|
| 426 |
+
"metric_unit": "proportion",
|
| 427 |
+
"lower_is_better": false,
|
| 428 |
+
"score_type": "continuous",
|
| 429 |
+
"min_score": 0.0,
|
| 430 |
+
"max_score": 1.0,
|
| 431 |
+
"additional_details": {
|
| 432 |
+
"aggregation": "accuracy_over_subset",
|
| 433 |
+
"prompt_style": "5-shot CoT"
|
| 434 |
+
}
|
| 435 |
+
},
|
| 436 |
+
"score_details": {
|
| 437 |
+
"score": 0.5174
|
| 438 |
+
}
|
| 439 |
+
},
|
| 440 |
+
{
|
| 441 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 442 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 443 |
+
"source_data": {
|
| 444 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 445 |
+
"source_type": "hf_dataset",
|
| 446 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 447 |
+
"hf_split": "train",
|
| 448 |
+
"additional_details": {
|
| 449 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 450 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 451 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 453 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 454 |
+
"dataset_total_questions": "12000",
|
| 455 |
+
"prompt_style": "5-shot CoT"
|
| 456 |
+
}
|
| 457 |
+
},
|
| 458 |
+
"metric_config": {
|
| 459 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 460 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 461 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 462 |
+
"metric_kind": "accuracy",
|
| 463 |
+
"metric_unit": "proportion",
|
| 464 |
+
"lower_is_better": false,
|
| 465 |
+
"score_type": "continuous",
|
| 466 |
+
"min_score": 0.0,
|
| 467 |
+
"max_score": 1.0,
|
| 468 |
+
"additional_details": {
|
| 469 |
+
"aggregation": "accuracy_over_subset",
|
| 470 |
+
"prompt_style": "5-shot CoT"
|
| 471 |
+
}
|
| 472 |
+
},
|
| 473 |
+
"score_details": {
|
| 474 |
+
"score": 0.7234
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 479 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 480 |
+
"source_data": {
|
| 481 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 482 |
+
"source_type": "hf_dataset",
|
| 483 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 484 |
+
"hf_split": "train",
|
| 485 |
+
"additional_details": {
|
| 486 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 487 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 488 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 490 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 491 |
+
"dataset_total_questions": "12000",
|
| 492 |
+
"prompt_style": "5-shot CoT"
|
| 493 |
+
}
|
| 494 |
+
},
|
| 495 |
+
"metric_config": {
|
| 496 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 497 |
+
"metric_id": "mmlu_pro/physics",
|
| 498 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 499 |
+
"metric_kind": "accuracy",
|
| 500 |
+
"metric_unit": "proportion",
|
| 501 |
+
"lower_is_better": false,
|
| 502 |
+
"score_type": "continuous",
|
| 503 |
+
"min_score": 0.0,
|
| 504 |
+
"max_score": 1.0,
|
| 505 |
+
"additional_details": {
|
| 506 |
+
"aggregation": "accuracy_over_subset",
|
| 507 |
+
"prompt_style": "5-shot CoT"
|
| 508 |
+
}
|
| 509 |
+
},
|
| 510 |
+
"score_details": {
|
| 511 |
+
"score": 0.8072
|
| 512 |
+
}
|
| 513 |
+
},
|
| 514 |
+
{
|
| 515 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 516 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 517 |
+
"source_data": {
|
| 518 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 519 |
+
"source_type": "hf_dataset",
|
| 520 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 521 |
+
"hf_split": "train",
|
| 522 |
+
"additional_details": {
|
| 523 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 524 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 525 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 527 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 528 |
+
"dataset_total_questions": "12000",
|
| 529 |
+
"prompt_style": "5-shot CoT"
|
| 530 |
+
}
|
| 531 |
+
},
|
| 532 |
+
"metric_config": {
|
| 533 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 534 |
+
"metric_id": "mmlu_pro/psychology",
|
| 535 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 536 |
+
"metric_kind": "accuracy",
|
| 537 |
+
"metric_unit": "proportion",
|
| 538 |
+
"lower_is_better": false,
|
| 539 |
+
"score_type": "continuous",
|
| 540 |
+
"min_score": 0.0,
|
| 541 |
+
"max_score": 1.0,
|
| 542 |
+
"additional_details": {
|
| 543 |
+
"aggregation": "accuracy_over_subset",
|
| 544 |
+
"prompt_style": "5-shot CoT"
|
| 545 |
+
}
|
| 546 |
+
},
|
| 547 |
+
"score_details": {
|
| 548 |
+
"score": 0.8294
|
| 549 |
+
}
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 553 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 554 |
+
"source_data": {
|
| 555 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 556 |
+
"source_type": "hf_dataset",
|
| 557 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 558 |
+
"hf_split": "train",
|
| 559 |
+
"additional_details": {
|
| 560 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 561 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 562 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 564 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 565 |
+
"dataset_total_questions": "12000",
|
| 566 |
+
"prompt_style": "5-shot CoT"
|
| 567 |
+
}
|
| 568 |
+
},
|
| 569 |
+
"metric_config": {
|
| 570 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 571 |
+
"metric_id": "mmlu_pro/other",
|
| 572 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 573 |
+
"metric_kind": "accuracy",
|
| 574 |
+
"metric_unit": "proportion",
|
| 575 |
+
"lower_is_better": false,
|
| 576 |
+
"score_type": "continuous",
|
| 577 |
+
"min_score": 0.0,
|
| 578 |
+
"max_score": 1.0,
|
| 579 |
+
"additional_details": {
|
| 580 |
+
"aggregation": "accuracy_over_subset",
|
| 581 |
+
"prompt_style": "5-shot CoT"
|
| 582 |
+
}
|
| 583 |
+
},
|
| 584 |
+
"score_details": {
|
| 585 |
+
"score": 0.7359
|
| 586 |
+
}
|
| 587 |
+
}
|
| 588 |
+
]
|
| 589 |
+
}
|
data/mmlu_pro/google/gemini-1.5-pro/fd7ad2ae-dd54-4275-8db7-7da0110eb2be.json
ADDED
|
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-1.5-pro/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-1.5-Pro",
|
| 25 |
+
"id": "google/gemini-1.5-pro",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-1.5-Pro",
|
| 29 |
+
"leaderboard_data_source": "Self-Reported"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.6903
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 72 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 73 |
+
"source_data": {
|
| 74 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 75 |
+
"source_type": "hf_dataset",
|
| 76 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 77 |
+
"hf_split": "train",
|
| 78 |
+
"additional_details": {
|
| 79 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 80 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 81 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 83 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 84 |
+
"dataset_total_questions": "12000",
|
| 85 |
+
"prompt_style": "5-shot CoT"
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"metric_config": {
|
| 89 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 90 |
+
"metric_id": "mmlu_pro/biology",
|
| 91 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 92 |
+
"metric_kind": "accuracy",
|
| 93 |
+
"metric_unit": "proportion",
|
| 94 |
+
"lower_is_better": false,
|
| 95 |
+
"score_type": "continuous",
|
| 96 |
+
"min_score": 0.0,
|
| 97 |
+
"max_score": 1.0,
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"aggregation": "accuracy_over_subset",
|
| 100 |
+
"prompt_style": "5-shot CoT"
|
| 101 |
+
}
|
| 102 |
+
},
|
| 103 |
+
"score_details": {
|
| 104 |
+
"score": 0.8466
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 109 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 110 |
+
"source_data": {
|
| 111 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 112 |
+
"source_type": "hf_dataset",
|
| 113 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 114 |
+
"hf_split": "train",
|
| 115 |
+
"additional_details": {
|
| 116 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 117 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 118 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 120 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 121 |
+
"dataset_total_questions": "12000",
|
| 122 |
+
"prompt_style": "5-shot CoT"
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"metric_config": {
|
| 126 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 127 |
+
"metric_id": "mmlu_pro/business",
|
| 128 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 129 |
+
"metric_kind": "accuracy",
|
| 130 |
+
"metric_unit": "proportion",
|
| 131 |
+
"lower_is_better": false,
|
| 132 |
+
"score_type": "continuous",
|
| 133 |
+
"min_score": 0.0,
|
| 134 |
+
"max_score": 1.0,
|
| 135 |
+
"additional_details": {
|
| 136 |
+
"aggregation": "accuracy_over_subset",
|
| 137 |
+
"prompt_style": "5-shot CoT"
|
| 138 |
+
}
|
| 139 |
+
},
|
| 140 |
+
"score_details": {
|
| 141 |
+
"score": 0.7288
|
| 142 |
+
}
|
| 143 |
+
},
|
| 144 |
+
{
|
| 145 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 146 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 147 |
+
"source_data": {
|
| 148 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 151 |
+
"hf_split": "train",
|
| 152 |
+
"additional_details": {
|
| 153 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 154 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 155 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 157 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 158 |
+
"dataset_total_questions": "12000",
|
| 159 |
+
"prompt_style": "5-shot CoT"
|
| 160 |
+
}
|
| 161 |
+
},
|
| 162 |
+
"metric_config": {
|
| 163 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 164 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 165 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 166 |
+
"metric_kind": "accuracy",
|
| 167 |
+
"metric_unit": "proportion",
|
| 168 |
+
"lower_is_better": false,
|
| 169 |
+
"score_type": "continuous",
|
| 170 |
+
"min_score": 0.0,
|
| 171 |
+
"max_score": 1.0,
|
| 172 |
+
"additional_details": {
|
| 173 |
+
"aggregation": "accuracy_over_subset",
|
| 174 |
+
"prompt_style": "5-shot CoT"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"score_details": {
|
| 178 |
+
"score": 0.7032
|
| 179 |
+
}
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 183 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 184 |
+
"source_data": {
|
| 185 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 186 |
+
"source_type": "hf_dataset",
|
| 187 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 188 |
+
"hf_split": "train",
|
| 189 |
+
"additional_details": {
|
| 190 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 191 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 192 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 194 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 195 |
+
"dataset_total_questions": "12000",
|
| 196 |
+
"prompt_style": "5-shot CoT"
|
| 197 |
+
}
|
| 198 |
+
},
|
| 199 |
+
"metric_config": {
|
| 200 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 201 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 202 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 203 |
+
"metric_kind": "accuracy",
|
| 204 |
+
"metric_unit": "proportion",
|
| 205 |
+
"lower_is_better": false,
|
| 206 |
+
"score_type": "continuous",
|
| 207 |
+
"min_score": 0.0,
|
| 208 |
+
"max_score": 1.0,
|
| 209 |
+
"additional_details": {
|
| 210 |
+
"aggregation": "accuracy_over_subset",
|
| 211 |
+
"prompt_style": "5-shot CoT"
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"score_details": {
|
| 215 |
+
"score": 0.7293
|
| 216 |
+
}
|
| 217 |
+
},
|
| 218 |
+
{
|
| 219 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 220 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 221 |
+
"source_data": {
|
| 222 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 223 |
+
"source_type": "hf_dataset",
|
| 224 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 225 |
+
"hf_split": "train",
|
| 226 |
+
"additional_details": {
|
| 227 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 228 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 229 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 231 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 232 |
+
"dataset_total_questions": "12000",
|
| 233 |
+
"prompt_style": "5-shot CoT"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"metric_config": {
|
| 237 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 238 |
+
"metric_id": "mmlu_pro/economics",
|
| 239 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 240 |
+
"metric_kind": "accuracy",
|
| 241 |
+
"metric_unit": "proportion",
|
| 242 |
+
"lower_is_better": false,
|
| 243 |
+
"score_type": "continuous",
|
| 244 |
+
"min_score": 0.0,
|
| 245 |
+
"max_score": 1.0,
|
| 246 |
+
"additional_details": {
|
| 247 |
+
"aggregation": "accuracy_over_subset",
|
| 248 |
+
"prompt_style": "5-shot CoT"
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"score_details": {
|
| 252 |
+
"score": 0.7844
|
| 253 |
+
}
|
| 254 |
+
},
|
| 255 |
+
{
|
| 256 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 257 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 258 |
+
"source_data": {
|
| 259 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 260 |
+
"source_type": "hf_dataset",
|
| 261 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 262 |
+
"hf_split": "train",
|
| 263 |
+
"additional_details": {
|
| 264 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 265 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 266 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 268 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 269 |
+
"dataset_total_questions": "12000",
|
| 270 |
+
"prompt_style": "5-shot CoT"
|
| 271 |
+
}
|
| 272 |
+
},
|
| 273 |
+
"metric_config": {
|
| 274 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 275 |
+
"metric_id": "mmlu_pro/engineering",
|
| 276 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 277 |
+
"metric_kind": "accuracy",
|
| 278 |
+
"metric_unit": "proportion",
|
| 279 |
+
"lower_is_better": false,
|
| 280 |
+
"score_type": "continuous",
|
| 281 |
+
"min_score": 0.0,
|
| 282 |
+
"max_score": 1.0,
|
| 283 |
+
"additional_details": {
|
| 284 |
+
"aggregation": "accuracy_over_subset",
|
| 285 |
+
"prompt_style": "5-shot CoT"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"score_details": {
|
| 289 |
+
"score": 0.4871
|
| 290 |
+
}
|
| 291 |
+
},
|
| 292 |
+
{
|
| 293 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 294 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 295 |
+
"source_data": {
|
| 296 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 297 |
+
"source_type": "hf_dataset",
|
| 298 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 299 |
+
"hf_split": "train",
|
| 300 |
+
"additional_details": {
|
| 301 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 302 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 303 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 305 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 306 |
+
"dataset_total_questions": "12000",
|
| 307 |
+
"prompt_style": "5-shot CoT"
|
| 308 |
+
}
|
| 309 |
+
},
|
| 310 |
+
"metric_config": {
|
| 311 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 312 |
+
"metric_id": "mmlu_pro/health",
|
| 313 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 314 |
+
"metric_kind": "accuracy",
|
| 315 |
+
"metric_unit": "proportion",
|
| 316 |
+
"lower_is_better": false,
|
| 317 |
+
"score_type": "continuous",
|
| 318 |
+
"min_score": 0.0,
|
| 319 |
+
"max_score": 1.0,
|
| 320 |
+
"additional_details": {
|
| 321 |
+
"aggregation": "accuracy_over_subset",
|
| 322 |
+
"prompt_style": "5-shot CoT"
|
| 323 |
+
}
|
| 324 |
+
},
|
| 325 |
+
"score_details": {
|
| 326 |
+
"score": 0.7274
|
| 327 |
+
}
|
| 328 |
+
},
|
| 329 |
+
{
|
| 330 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 331 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 332 |
+
"source_data": {
|
| 333 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 334 |
+
"source_type": "hf_dataset",
|
| 335 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 336 |
+
"hf_split": "train",
|
| 337 |
+
"additional_details": {
|
| 338 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 339 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 340 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 342 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 343 |
+
"dataset_total_questions": "12000",
|
| 344 |
+
"prompt_style": "5-shot CoT"
|
| 345 |
+
}
|
| 346 |
+
},
|
| 347 |
+
"metric_config": {
|
| 348 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 349 |
+
"metric_id": "mmlu_pro/history",
|
| 350 |
+
"metric_name": "MMLU-Pro (History)",
|
| 351 |
+
"metric_kind": "accuracy",
|
| 352 |
+
"metric_unit": "proportion",
|
| 353 |
+
"lower_is_better": false,
|
| 354 |
+
"score_type": "continuous",
|
| 355 |
+
"min_score": 0.0,
|
| 356 |
+
"max_score": 1.0,
|
| 357 |
+
"additional_details": {
|
| 358 |
+
"aggregation": "accuracy_over_subset",
|
| 359 |
+
"prompt_style": "5-shot CoT"
|
| 360 |
+
}
|
| 361 |
+
},
|
| 362 |
+
"score_details": {
|
| 363 |
+
"score": 0.6562
|
| 364 |
+
}
|
| 365 |
+
},
|
| 366 |
+
{
|
| 367 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 368 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 369 |
+
"source_data": {
|
| 370 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 371 |
+
"source_type": "hf_dataset",
|
| 372 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 373 |
+
"hf_split": "train",
|
| 374 |
+
"additional_details": {
|
| 375 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 376 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 377 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 379 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 380 |
+
"dataset_total_questions": "12000",
|
| 381 |
+
"prompt_style": "5-shot CoT"
|
| 382 |
+
}
|
| 383 |
+
},
|
| 384 |
+
"metric_config": {
|
| 385 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 386 |
+
"metric_id": "mmlu_pro/law",
|
| 387 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 388 |
+
"metric_kind": "accuracy",
|
| 389 |
+
"metric_unit": "proportion",
|
| 390 |
+
"lower_is_better": false,
|
| 391 |
+
"score_type": "continuous",
|
| 392 |
+
"min_score": 0.0,
|
| 393 |
+
"max_score": 1.0,
|
| 394 |
+
"additional_details": {
|
| 395 |
+
"aggregation": "accuracy_over_subset",
|
| 396 |
+
"prompt_style": "5-shot CoT"
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"score_details": {
|
| 400 |
+
"score": 0.5077
|
| 401 |
+
}
|
| 402 |
+
},
|
| 403 |
+
{
|
| 404 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 405 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 406 |
+
"source_data": {
|
| 407 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 408 |
+
"source_type": "hf_dataset",
|
| 409 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 410 |
+
"hf_split": "train",
|
| 411 |
+
"additional_details": {
|
| 412 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 413 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 414 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 416 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 417 |
+
"dataset_total_questions": "12000",
|
| 418 |
+
"prompt_style": "5-shot CoT"
|
| 419 |
+
}
|
| 420 |
+
},
|
| 421 |
+
"metric_config": {
|
| 422 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 423 |
+
"metric_id": "mmlu_pro/math",
|
| 424 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 425 |
+
"metric_kind": "accuracy",
|
| 426 |
+
"metric_unit": "proportion",
|
| 427 |
+
"lower_is_better": false,
|
| 428 |
+
"score_type": "continuous",
|
| 429 |
+
"min_score": 0.0,
|
| 430 |
+
"max_score": 1.0,
|
| 431 |
+
"additional_details": {
|
| 432 |
+
"aggregation": "accuracy_over_subset",
|
| 433 |
+
"prompt_style": "5-shot CoT"
|
| 434 |
+
}
|
| 435 |
+
},
|
| 436 |
+
"score_details": {
|
| 437 |
+
"score": 0.7276
|
| 438 |
+
}
|
| 439 |
+
},
|
| 440 |
+
{
|
| 441 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 442 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 443 |
+
"source_data": {
|
| 444 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 445 |
+
"source_type": "hf_dataset",
|
| 446 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 447 |
+
"hf_split": "train",
|
| 448 |
+
"additional_details": {
|
| 449 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 450 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 451 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 453 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 454 |
+
"dataset_total_questions": "12000",
|
| 455 |
+
"prompt_style": "5-shot CoT"
|
| 456 |
+
}
|
| 457 |
+
},
|
| 458 |
+
"metric_config": {
|
| 459 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 460 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 461 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 462 |
+
"metric_kind": "accuracy",
|
| 463 |
+
"metric_unit": "proportion",
|
| 464 |
+
"lower_is_better": false,
|
| 465 |
+
"score_type": "continuous",
|
| 466 |
+
"min_score": 0.0,
|
| 467 |
+
"max_score": 1.0,
|
| 468 |
+
"additional_details": {
|
| 469 |
+
"aggregation": "accuracy_over_subset",
|
| 470 |
+
"prompt_style": "5-shot CoT"
|
| 471 |
+
}
|
| 472 |
+
},
|
| 473 |
+
"score_details": {
|
| 474 |
+
"score": 0.6172
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 479 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 480 |
+
"source_data": {
|
| 481 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 482 |
+
"source_type": "hf_dataset",
|
| 483 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 484 |
+
"hf_split": "train",
|
| 485 |
+
"additional_details": {
|
| 486 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 487 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 488 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 490 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 491 |
+
"dataset_total_questions": "12000",
|
| 492 |
+
"prompt_style": "5-shot CoT"
|
| 493 |
+
}
|
| 494 |
+
},
|
| 495 |
+
"metric_config": {
|
| 496 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 497 |
+
"metric_id": "mmlu_pro/physics",
|
| 498 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 499 |
+
"metric_kind": "accuracy",
|
| 500 |
+
"metric_unit": "proportion",
|
| 501 |
+
"lower_is_better": false,
|
| 502 |
+
"score_type": "continuous",
|
| 503 |
+
"min_score": 0.0,
|
| 504 |
+
"max_score": 1.0,
|
| 505 |
+
"additional_details": {
|
| 506 |
+
"aggregation": "accuracy_over_subset",
|
| 507 |
+
"prompt_style": "5-shot CoT"
|
| 508 |
+
}
|
| 509 |
+
},
|
| 510 |
+
"score_details": {
|
| 511 |
+
"score": 0.7036
|
| 512 |
+
}
|
| 513 |
+
},
|
| 514 |
+
{
|
| 515 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 516 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 517 |
+
"source_data": {
|
| 518 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 519 |
+
"source_type": "hf_dataset",
|
| 520 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 521 |
+
"hf_split": "train",
|
| 522 |
+
"additional_details": {
|
| 523 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 524 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 525 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 527 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 528 |
+
"dataset_total_questions": "12000",
|
| 529 |
+
"prompt_style": "5-shot CoT"
|
| 530 |
+
}
|
| 531 |
+
},
|
| 532 |
+
"metric_config": {
|
| 533 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 534 |
+
"metric_id": "mmlu_pro/psychology",
|
| 535 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 536 |
+
"metric_kind": "accuracy",
|
| 537 |
+
"metric_unit": "proportion",
|
| 538 |
+
"lower_is_better": false,
|
| 539 |
+
"score_type": "continuous",
|
| 540 |
+
"min_score": 0.0,
|
| 541 |
+
"max_score": 1.0,
|
| 542 |
+
"additional_details": {
|
| 543 |
+
"aggregation": "accuracy_over_subset",
|
| 544 |
+
"prompt_style": "5-shot CoT"
|
| 545 |
+
}
|
| 546 |
+
},
|
| 547 |
+
"score_details": {
|
| 548 |
+
"score": 0.772
|
| 549 |
+
}
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 553 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 554 |
+
"source_data": {
|
| 555 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 556 |
+
"source_type": "hf_dataset",
|
| 557 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 558 |
+
"hf_split": "train",
|
| 559 |
+
"additional_details": {
|
| 560 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 561 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 562 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 564 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 565 |
+
"dataset_total_questions": "12000",
|
| 566 |
+
"prompt_style": "5-shot CoT"
|
| 567 |
+
}
|
| 568 |
+
},
|
| 569 |
+
"metric_config": {
|
| 570 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 571 |
+
"metric_id": "mmlu_pro/other",
|
| 572 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 573 |
+
"metric_kind": "accuracy",
|
| 574 |
+
"metric_unit": "proportion",
|
| 575 |
+
"lower_is_better": false,
|
| 576 |
+
"score_type": "continuous",
|
| 577 |
+
"min_score": 0.0,
|
| 578 |
+
"max_score": 1.0,
|
| 579 |
+
"additional_details": {
|
| 580 |
+
"aggregation": "accuracy_over_subset",
|
| 581 |
+
"prompt_style": "5-shot CoT"
|
| 582 |
+
}
|
| 583 |
+
},
|
| 584 |
+
"score_details": {
|
| 585 |
+
"score": 0.7251
|
| 586 |
+
}
|
| 587 |
+
}
|
| 588 |
+
]
|
| 589 |
+
}
|
data/mmlu_pro/google/gemini-2.0-flash-exp/b70dfa85-e4ee-4eaa-9d5b-7f58a6ac2413.json
ADDED
|
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-2.0-flash-exp/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-2.0-Flash-exp",
|
| 25 |
+
"id": "google/gemini-2.0-flash-exp",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-2.0-Flash-exp",
|
| 29 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.7624
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 72 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 73 |
+
"source_data": {
|
| 74 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 75 |
+
"source_type": "hf_dataset",
|
| 76 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 77 |
+
"hf_split": "train",
|
| 78 |
+
"additional_details": {
|
| 79 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 80 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 81 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 83 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 84 |
+
"dataset_total_questions": "12000",
|
| 85 |
+
"prompt_style": "5-shot CoT"
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"metric_config": {
|
| 89 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 90 |
+
"metric_id": "mmlu_pro/biology",
|
| 91 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 92 |
+
"metric_kind": "accuracy",
|
| 93 |
+
"metric_unit": "proportion",
|
| 94 |
+
"lower_is_better": false,
|
| 95 |
+
"score_type": "continuous",
|
| 96 |
+
"min_score": 0.0,
|
| 97 |
+
"max_score": 1.0,
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"aggregation": "accuracy_over_subset",
|
| 100 |
+
"prompt_style": "5-shot CoT"
|
| 101 |
+
}
|
| 102 |
+
},
|
| 103 |
+
"score_details": {
|
| 104 |
+
"score": 0.8836
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 109 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 110 |
+
"source_data": {
|
| 111 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 112 |
+
"source_type": "hf_dataset",
|
| 113 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 114 |
+
"hf_split": "train",
|
| 115 |
+
"additional_details": {
|
| 116 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 117 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 118 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 120 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 121 |
+
"dataset_total_questions": "12000",
|
| 122 |
+
"prompt_style": "5-shot CoT"
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"metric_config": {
|
| 126 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 127 |
+
"metric_id": "mmlu_pro/business",
|
| 128 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 129 |
+
"metric_kind": "accuracy",
|
| 130 |
+
"metric_unit": "proportion",
|
| 131 |
+
"lower_is_better": false,
|
| 132 |
+
"score_type": "continuous",
|
| 133 |
+
"min_score": 0.0,
|
| 134 |
+
"max_score": 1.0,
|
| 135 |
+
"additional_details": {
|
| 136 |
+
"aggregation": "accuracy_over_subset",
|
| 137 |
+
"prompt_style": "5-shot CoT"
|
| 138 |
+
}
|
| 139 |
+
},
|
| 140 |
+
"score_details": {
|
| 141 |
+
"score": 0.7985
|
| 142 |
+
}
|
| 143 |
+
},
|
| 144 |
+
{
|
| 145 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 146 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 147 |
+
"source_data": {
|
| 148 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 151 |
+
"hf_split": "train",
|
| 152 |
+
"additional_details": {
|
| 153 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 154 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 155 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 157 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 158 |
+
"dataset_total_questions": "12000",
|
| 159 |
+
"prompt_style": "5-shot CoT"
|
| 160 |
+
}
|
| 161 |
+
},
|
| 162 |
+
"metric_config": {
|
| 163 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 164 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 165 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 166 |
+
"metric_kind": "accuracy",
|
| 167 |
+
"metric_unit": "proportion",
|
| 168 |
+
"lower_is_better": false,
|
| 169 |
+
"score_type": "continuous",
|
| 170 |
+
"min_score": 0.0,
|
| 171 |
+
"max_score": 1.0,
|
| 172 |
+
"additional_details": {
|
| 173 |
+
"aggregation": "accuracy_over_subset",
|
| 174 |
+
"prompt_style": "5-shot CoT"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"score_details": {
|
| 178 |
+
"score": 0.8004
|
| 179 |
+
}
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 183 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 184 |
+
"source_data": {
|
| 185 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 186 |
+
"source_type": "hf_dataset",
|
| 187 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 188 |
+
"hf_split": "train",
|
| 189 |
+
"additional_details": {
|
| 190 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 191 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 192 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 194 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 195 |
+
"dataset_total_questions": "12000",
|
| 196 |
+
"prompt_style": "5-shot CoT"
|
| 197 |
+
}
|
| 198 |
+
},
|
| 199 |
+
"metric_config": {
|
| 200 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 201 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 202 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 203 |
+
"metric_kind": "accuracy",
|
| 204 |
+
"metric_unit": "proportion",
|
| 205 |
+
"lower_is_better": false,
|
| 206 |
+
"score_type": "continuous",
|
| 207 |
+
"min_score": 0.0,
|
| 208 |
+
"max_score": 1.0,
|
| 209 |
+
"additional_details": {
|
| 210 |
+
"aggregation": "accuracy_over_subset",
|
| 211 |
+
"prompt_style": "5-shot CoT"
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"score_details": {
|
| 215 |
+
"score": 0.799
|
| 216 |
+
}
|
| 217 |
+
},
|
| 218 |
+
{
|
| 219 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 220 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 221 |
+
"source_data": {
|
| 222 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 223 |
+
"source_type": "hf_dataset",
|
| 224 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 225 |
+
"hf_split": "train",
|
| 226 |
+
"additional_details": {
|
| 227 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 228 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 229 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 231 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 232 |
+
"dataset_total_questions": "12000",
|
| 233 |
+
"prompt_style": "5-shot CoT"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"metric_config": {
|
| 237 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 238 |
+
"metric_id": "mmlu_pro/economics",
|
| 239 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 240 |
+
"metric_kind": "accuracy",
|
| 241 |
+
"metric_unit": "proportion",
|
| 242 |
+
"lower_is_better": false,
|
| 243 |
+
"score_type": "continuous",
|
| 244 |
+
"min_score": 0.0,
|
| 245 |
+
"max_score": 1.0,
|
| 246 |
+
"additional_details": {
|
| 247 |
+
"aggregation": "accuracy_over_subset",
|
| 248 |
+
"prompt_style": "5-shot CoT"
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"score_details": {
|
| 252 |
+
"score": 0.8169
|
| 253 |
+
}
|
| 254 |
+
},
|
| 255 |
+
{
|
| 256 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 257 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 258 |
+
"source_data": {
|
| 259 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 260 |
+
"source_type": "hf_dataset",
|
| 261 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 262 |
+
"hf_split": "train",
|
| 263 |
+
"additional_details": {
|
| 264 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 265 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 266 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 268 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 269 |
+
"dataset_total_questions": "12000",
|
| 270 |
+
"prompt_style": "5-shot CoT"
|
| 271 |
+
}
|
| 272 |
+
},
|
| 273 |
+
"metric_config": {
|
| 274 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 275 |
+
"metric_id": "mmlu_pro/engineering",
|
| 276 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 277 |
+
"metric_kind": "accuracy",
|
| 278 |
+
"metric_unit": "proportion",
|
| 279 |
+
"lower_is_better": false,
|
| 280 |
+
"score_type": "continuous",
|
| 281 |
+
"min_score": 0.0,
|
| 282 |
+
"max_score": 1.0,
|
| 283 |
+
"additional_details": {
|
| 284 |
+
"aggregation": "accuracy_over_subset",
|
| 285 |
+
"prompt_style": "5-shot CoT"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"score_details": {
|
| 289 |
+
"score": 0.6155
|
| 290 |
+
}
|
| 291 |
+
},
|
| 292 |
+
{
|
| 293 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 294 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 295 |
+
"source_data": {
|
| 296 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 297 |
+
"source_type": "hf_dataset",
|
| 298 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 299 |
+
"hf_split": "train",
|
| 300 |
+
"additional_details": {
|
| 301 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 302 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 303 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 305 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 306 |
+
"dataset_total_questions": "12000",
|
| 307 |
+
"prompt_style": "5-shot CoT"
|
| 308 |
+
}
|
| 309 |
+
},
|
| 310 |
+
"metric_config": {
|
| 311 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 312 |
+
"metric_id": "mmlu_pro/health",
|
| 313 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 314 |
+
"metric_kind": "accuracy",
|
| 315 |
+
"metric_unit": "proportion",
|
| 316 |
+
"lower_is_better": false,
|
| 317 |
+
"score_type": "continuous",
|
| 318 |
+
"min_score": 0.0,
|
| 319 |
+
"max_score": 1.0,
|
| 320 |
+
"additional_details": {
|
| 321 |
+
"aggregation": "accuracy_over_subset",
|
| 322 |
+
"prompt_style": "5-shot CoT"
|
| 323 |
+
}
|
| 324 |
+
},
|
| 325 |
+
"score_details": {
|
| 326 |
+
"score": 0.7442
|
| 327 |
+
}
|
| 328 |
+
},
|
| 329 |
+
{
|
| 330 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 331 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 332 |
+
"source_data": {
|
| 333 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 334 |
+
"source_type": "hf_dataset",
|
| 335 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 336 |
+
"hf_split": "train",
|
| 337 |
+
"additional_details": {
|
| 338 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 339 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 340 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 342 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 343 |
+
"dataset_total_questions": "12000",
|
| 344 |
+
"prompt_style": "5-shot CoT"
|
| 345 |
+
}
|
| 346 |
+
},
|
| 347 |
+
"metric_config": {
|
| 348 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 349 |
+
"metric_id": "mmlu_pro/history",
|
| 350 |
+
"metric_name": "MMLU-Pro (History)",
|
| 351 |
+
"metric_kind": "accuracy",
|
| 352 |
+
"metric_unit": "proportion",
|
| 353 |
+
"lower_is_better": false,
|
| 354 |
+
"score_type": "continuous",
|
| 355 |
+
"min_score": 0.0,
|
| 356 |
+
"max_score": 1.0,
|
| 357 |
+
"additional_details": {
|
| 358 |
+
"aggregation": "accuracy_over_subset",
|
| 359 |
+
"prompt_style": "5-shot CoT"
|
| 360 |
+
}
|
| 361 |
+
},
|
| 362 |
+
"score_details": {
|
| 363 |
+
"score": 0.7008
|
| 364 |
+
}
|
| 365 |
+
},
|
| 366 |
+
{
|
| 367 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 368 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 369 |
+
"source_data": {
|
| 370 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 371 |
+
"source_type": "hf_dataset",
|
| 372 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 373 |
+
"hf_split": "train",
|
| 374 |
+
"additional_details": {
|
| 375 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 376 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 377 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 379 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 380 |
+
"dataset_total_questions": "12000",
|
| 381 |
+
"prompt_style": "5-shot CoT"
|
| 382 |
+
}
|
| 383 |
+
},
|
| 384 |
+
"metric_config": {
|
| 385 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 386 |
+
"metric_id": "mmlu_pro/law",
|
| 387 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 388 |
+
"metric_kind": "accuracy",
|
| 389 |
+
"metric_unit": "proportion",
|
| 390 |
+
"lower_is_better": false,
|
| 391 |
+
"score_type": "continuous",
|
| 392 |
+
"min_score": 0.0,
|
| 393 |
+
"max_score": 1.0,
|
| 394 |
+
"additional_details": {
|
| 395 |
+
"aggregation": "accuracy_over_subset",
|
| 396 |
+
"prompt_style": "5-shot CoT"
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"score_details": {
|
| 400 |
+
"score": 0.5647
|
| 401 |
+
}
|
| 402 |
+
},
|
| 403 |
+
{
|
| 404 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 405 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 406 |
+
"source_data": {
|
| 407 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 408 |
+
"source_type": "hf_dataset",
|
| 409 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 410 |
+
"hf_split": "train",
|
| 411 |
+
"additional_details": {
|
| 412 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 413 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 414 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 416 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 417 |
+
"dataset_total_questions": "12000",
|
| 418 |
+
"prompt_style": "5-shot CoT"
|
| 419 |
+
}
|
| 420 |
+
},
|
| 421 |
+
"metric_config": {
|
| 422 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 423 |
+
"metric_id": "mmlu_pro/math",
|
| 424 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 425 |
+
"metric_kind": "accuracy",
|
| 426 |
+
"metric_unit": "proportion",
|
| 427 |
+
"lower_is_better": false,
|
| 428 |
+
"score_type": "continuous",
|
| 429 |
+
"min_score": 0.0,
|
| 430 |
+
"max_score": 1.0,
|
| 431 |
+
"additional_details": {
|
| 432 |
+
"aggregation": "accuracy_over_subset",
|
| 433 |
+
"prompt_style": "5-shot CoT"
|
| 434 |
+
}
|
| 435 |
+
},
|
| 436 |
+
"score_details": {
|
| 437 |
+
"score": 0.8638
|
| 438 |
+
}
|
| 439 |
+
},
|
| 440 |
+
{
|
| 441 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 442 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 443 |
+
"source_data": {
|
| 444 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 445 |
+
"source_type": "hf_dataset",
|
| 446 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 447 |
+
"hf_split": "train",
|
| 448 |
+
"additional_details": {
|
| 449 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 450 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 451 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 453 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 454 |
+
"dataset_total_questions": "12000",
|
| 455 |
+
"prompt_style": "5-shot CoT"
|
| 456 |
+
}
|
| 457 |
+
},
|
| 458 |
+
"metric_config": {
|
| 459 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 460 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 461 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 462 |
+
"metric_kind": "accuracy",
|
| 463 |
+
"metric_unit": "proportion",
|
| 464 |
+
"lower_is_better": false,
|
| 465 |
+
"score_type": "continuous",
|
| 466 |
+
"min_score": 0.0,
|
| 467 |
+
"max_score": 1.0,
|
| 468 |
+
"additional_details": {
|
| 469 |
+
"aggregation": "accuracy_over_subset",
|
| 470 |
+
"prompt_style": "5-shot CoT"
|
| 471 |
+
}
|
| 472 |
+
},
|
| 473 |
+
"score_details": {
|
| 474 |
+
"score": 0.6994
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 479 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 480 |
+
"source_data": {
|
| 481 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 482 |
+
"source_type": "hf_dataset",
|
| 483 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 484 |
+
"hf_split": "train",
|
| 485 |
+
"additional_details": {
|
| 486 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 487 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 488 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 490 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 491 |
+
"dataset_total_questions": "12000",
|
| 492 |
+
"prompt_style": "5-shot CoT"
|
| 493 |
+
}
|
| 494 |
+
},
|
| 495 |
+
"metric_config": {
|
| 496 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 497 |
+
"metric_id": "mmlu_pro/physics",
|
| 498 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 499 |
+
"metric_kind": "accuracy",
|
| 500 |
+
"metric_unit": "proportion",
|
| 501 |
+
"lower_is_better": false,
|
| 502 |
+
"score_type": "continuous",
|
| 503 |
+
"min_score": 0.0,
|
| 504 |
+
"max_score": 1.0,
|
| 505 |
+
"additional_details": {
|
| 506 |
+
"aggregation": "accuracy_over_subset",
|
| 507 |
+
"prompt_style": "5-shot CoT"
|
| 508 |
+
}
|
| 509 |
+
},
|
| 510 |
+
"score_details": {
|
| 511 |
+
"score": 0.8127
|
| 512 |
+
}
|
| 513 |
+
},
|
| 514 |
+
{
|
| 515 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 516 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 517 |
+
"source_data": {
|
| 518 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 519 |
+
"source_type": "hf_dataset",
|
| 520 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 521 |
+
"hf_split": "train",
|
| 522 |
+
"additional_details": {
|
| 523 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 524 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 525 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 527 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 528 |
+
"dataset_total_questions": "12000",
|
| 529 |
+
"prompt_style": "5-shot CoT"
|
| 530 |
+
}
|
| 531 |
+
},
|
| 532 |
+
"metric_config": {
|
| 533 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 534 |
+
"metric_id": "mmlu_pro/psychology",
|
| 535 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 536 |
+
"metric_kind": "accuracy",
|
| 537 |
+
"metric_unit": "proportion",
|
| 538 |
+
"lower_is_better": false,
|
| 539 |
+
"score_type": "continuous",
|
| 540 |
+
"min_score": 0.0,
|
| 541 |
+
"max_score": 1.0,
|
| 542 |
+
"additional_details": {
|
| 543 |
+
"aggregation": "accuracy_over_subset",
|
| 544 |
+
"prompt_style": "5-shot CoT"
|
| 545 |
+
}
|
| 546 |
+
},
|
| 547 |
+
"score_details": {
|
| 548 |
+
"score": 0.7905
|
| 549 |
+
}
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 553 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 554 |
+
"source_data": {
|
| 555 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 556 |
+
"source_type": "hf_dataset",
|
| 557 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 558 |
+
"hf_split": "train",
|
| 559 |
+
"additional_details": {
|
| 560 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 561 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 562 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 564 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 565 |
+
"dataset_total_questions": "12000",
|
| 566 |
+
"prompt_style": "5-shot CoT"
|
| 567 |
+
}
|
| 568 |
+
},
|
| 569 |
+
"metric_config": {
|
| 570 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 571 |
+
"metric_id": "mmlu_pro/other",
|
| 572 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 573 |
+
"metric_kind": "accuracy",
|
| 574 |
+
"metric_unit": "proportion",
|
| 575 |
+
"lower_is_better": false,
|
| 576 |
+
"score_type": "continuous",
|
| 577 |
+
"min_score": 0.0,
|
| 578 |
+
"max_score": 1.0,
|
| 579 |
+
"additional_details": {
|
| 580 |
+
"aggregation": "accuracy_over_subset",
|
| 581 |
+
"prompt_style": "5-shot CoT"
|
| 582 |
+
}
|
| 583 |
+
},
|
| 584 |
+
"score_details": {
|
| 585 |
+
"score": 0.7476
|
| 586 |
+
}
|
| 587 |
+
}
|
| 588 |
+
]
|
| 589 |
+
}
|
data/mmlu_pro/google/gemini-2.0-flash-lite/841ad2b0-86e3-44e4-88c2-d010d6c9c52e.json
ADDED
|
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-2.0-flash-lite/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-2.0-Flash-Lite",
|
| 25 |
+
"id": "google/gemini-2.0-flash-lite",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-2.0-Flash-Lite",
|
| 29 |
+
"leaderboard_data_source": "Self-Reported"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.716
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
]
|
| 71 |
+
}
|
data/mmlu_pro/google/gemini-2.0-flash/1961f28e-d519-422d-8221-fd482b2f60f5.json
ADDED
|
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-2.0-flash/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-2.0-Flash",
|
| 25 |
+
"id": "google/gemini-2.0-flash",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-2.0-Flash",
|
| 29 |
+
"leaderboard_data_source": "Self-Reported"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.776
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
]
|
| 71 |
+
}
|
data/mmlu_pro/google/gemini-2.0-pro/34eecaad-e501-4b2b-9e9c-284804d1a581.json
ADDED
|
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-2.0-pro/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-2.0-Pro",
|
| 25 |
+
"id": "google/gemini-2.0-pro",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-2.0-Pro",
|
| 29 |
+
"leaderboard_data_source": "Self-Reported"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.791
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
]
|
| 71 |
+
}
|
data/mmlu_pro/google/gemini-2.5-pro-exp-03-25/976b7201-8e01-49f0-b9ab-884d61e16288.json
ADDED
|
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-2.5-pro-exp-03-25/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-2.5-Pro-Exp-03-25",
|
| 25 |
+
"id": "google/gemini-2.5-pro-exp-03-25",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-2.5-Pro-Exp-03-25",
|
| 29 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.8452
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 72 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 73 |
+
"source_data": {
|
| 74 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 75 |
+
"source_type": "hf_dataset",
|
| 76 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 77 |
+
"hf_split": "train",
|
| 78 |
+
"additional_details": {
|
| 79 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 80 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 81 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 83 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 84 |
+
"dataset_total_questions": "12000",
|
| 85 |
+
"prompt_style": "5-shot CoT"
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"metric_config": {
|
| 89 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 90 |
+
"metric_id": "mmlu_pro/biology",
|
| 91 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 92 |
+
"metric_kind": "accuracy",
|
| 93 |
+
"metric_unit": "proportion",
|
| 94 |
+
"lower_is_better": false,
|
| 95 |
+
"score_type": "continuous",
|
| 96 |
+
"min_score": 0.0,
|
| 97 |
+
"max_score": 1.0,
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"aggregation": "accuracy_over_subset",
|
| 100 |
+
"prompt_style": "5-shot CoT"
|
| 101 |
+
}
|
| 102 |
+
},
|
| 103 |
+
"score_details": {
|
| 104 |
+
"score": 0.9299
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 109 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 110 |
+
"source_data": {
|
| 111 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 112 |
+
"source_type": "hf_dataset",
|
| 113 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 114 |
+
"hf_split": "train",
|
| 115 |
+
"additional_details": {
|
| 116 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 117 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 118 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 120 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 121 |
+
"dataset_total_questions": "12000",
|
| 122 |
+
"prompt_style": "5-shot CoT"
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"metric_config": {
|
| 126 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 127 |
+
"metric_id": "mmlu_pro/business",
|
| 128 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 129 |
+
"metric_kind": "accuracy",
|
| 130 |
+
"metric_unit": "proportion",
|
| 131 |
+
"lower_is_better": false,
|
| 132 |
+
"score_type": "continuous",
|
| 133 |
+
"min_score": 0.0,
|
| 134 |
+
"max_score": 1.0,
|
| 135 |
+
"additional_details": {
|
| 136 |
+
"aggregation": "accuracy_over_subset",
|
| 137 |
+
"prompt_style": "5-shot CoT"
|
| 138 |
+
}
|
| 139 |
+
},
|
| 140 |
+
"score_details": {
|
| 141 |
+
"score": 0.893
|
| 142 |
+
}
|
| 143 |
+
},
|
| 144 |
+
{
|
| 145 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 146 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 147 |
+
"source_data": {
|
| 148 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 151 |
+
"hf_split": "train",
|
| 152 |
+
"additional_details": {
|
| 153 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 154 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 155 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 157 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 158 |
+
"dataset_total_questions": "12000",
|
| 159 |
+
"prompt_style": "5-shot CoT"
|
| 160 |
+
}
|
| 161 |
+
},
|
| 162 |
+
"metric_config": {
|
| 163 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 164 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 165 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 166 |
+
"metric_kind": "accuracy",
|
| 167 |
+
"metric_unit": "proportion",
|
| 168 |
+
"lower_is_better": false,
|
| 169 |
+
"score_type": "continuous",
|
| 170 |
+
"min_score": 0.0,
|
| 171 |
+
"max_score": 1.0,
|
| 172 |
+
"additional_details": {
|
| 173 |
+
"aggregation": "accuracy_over_subset",
|
| 174 |
+
"prompt_style": "5-shot CoT"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"score_details": {
|
| 178 |
+
"score": 0.8741
|
| 179 |
+
}
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 183 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 184 |
+
"source_data": {
|
| 185 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 186 |
+
"source_type": "hf_dataset",
|
| 187 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 188 |
+
"hf_split": "train",
|
| 189 |
+
"additional_details": {
|
| 190 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 191 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 192 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 194 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 195 |
+
"dataset_total_questions": "12000",
|
| 196 |
+
"prompt_style": "5-shot CoT"
|
| 197 |
+
}
|
| 198 |
+
},
|
| 199 |
+
"metric_config": {
|
| 200 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 201 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 202 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 203 |
+
"metric_kind": "accuracy",
|
| 204 |
+
"metric_unit": "proportion",
|
| 205 |
+
"lower_is_better": false,
|
| 206 |
+
"score_type": "continuous",
|
| 207 |
+
"min_score": 0.0,
|
| 208 |
+
"max_score": 1.0,
|
| 209 |
+
"additional_details": {
|
| 210 |
+
"aggregation": "accuracy_over_subset",
|
| 211 |
+
"prompt_style": "5-shot CoT"
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"score_details": {
|
| 215 |
+
"score": 0.8556
|
| 216 |
+
}
|
| 217 |
+
},
|
| 218 |
+
{
|
| 219 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 220 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 221 |
+
"source_data": {
|
| 222 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 223 |
+
"source_type": "hf_dataset",
|
| 224 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 225 |
+
"hf_split": "train",
|
| 226 |
+
"additional_details": {
|
| 227 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 228 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 229 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 231 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 232 |
+
"dataset_total_questions": "12000",
|
| 233 |
+
"prompt_style": "5-shot CoT"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"metric_config": {
|
| 237 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 238 |
+
"metric_id": "mmlu_pro/economics",
|
| 239 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 240 |
+
"metric_kind": "accuracy",
|
| 241 |
+
"metric_unit": "proportion",
|
| 242 |
+
"lower_is_better": false,
|
| 243 |
+
"score_type": "continuous",
|
| 244 |
+
"min_score": 0.0,
|
| 245 |
+
"max_score": 1.0,
|
| 246 |
+
"additional_details": {
|
| 247 |
+
"aggregation": "accuracy_over_subset",
|
| 248 |
+
"prompt_style": "5-shot CoT"
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"score_details": {
|
| 252 |
+
"score": 0.8829
|
| 253 |
+
}
|
| 254 |
+
},
|
| 255 |
+
{
|
| 256 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 257 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 258 |
+
"source_data": {
|
| 259 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 260 |
+
"source_type": "hf_dataset",
|
| 261 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 262 |
+
"hf_split": "train",
|
| 263 |
+
"additional_details": {
|
| 264 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 265 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 266 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 268 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 269 |
+
"dataset_total_questions": "12000",
|
| 270 |
+
"prompt_style": "5-shot CoT"
|
| 271 |
+
}
|
| 272 |
+
},
|
| 273 |
+
"metric_config": {
|
| 274 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 275 |
+
"metric_id": "mmlu_pro/engineering",
|
| 276 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 277 |
+
"metric_kind": "accuracy",
|
| 278 |
+
"metric_unit": "proportion",
|
| 279 |
+
"lower_is_better": false,
|
| 280 |
+
"score_type": "continuous",
|
| 281 |
+
"min_score": 0.0,
|
| 282 |
+
"max_score": 1.0,
|
| 283 |
+
"additional_details": {
|
| 284 |
+
"aggregation": "accuracy_over_subset",
|
| 285 |
+
"prompt_style": "5-shot CoT"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"score_details": {
|
| 289 |
+
"score": 0.7358
|
| 290 |
+
}
|
| 291 |
+
},
|
| 292 |
+
{
|
| 293 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 294 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 295 |
+
"source_data": {
|
| 296 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 297 |
+
"source_type": "hf_dataset",
|
| 298 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 299 |
+
"hf_split": "train",
|
| 300 |
+
"additional_details": {
|
| 301 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 302 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 303 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 305 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 306 |
+
"dataset_total_questions": "12000",
|
| 307 |
+
"prompt_style": "5-shot CoT"
|
| 308 |
+
}
|
| 309 |
+
},
|
| 310 |
+
"metric_config": {
|
| 311 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 312 |
+
"metric_id": "mmlu_pro/health",
|
| 313 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 314 |
+
"metric_kind": "accuracy",
|
| 315 |
+
"metric_unit": "proportion",
|
| 316 |
+
"lower_is_better": false,
|
| 317 |
+
"score_type": "continuous",
|
| 318 |
+
"min_score": 0.0,
|
| 319 |
+
"max_score": 1.0,
|
| 320 |
+
"additional_details": {
|
| 321 |
+
"aggregation": "accuracy_over_subset",
|
| 322 |
+
"prompt_style": "5-shot CoT"
|
| 323 |
+
}
|
| 324 |
+
},
|
| 325 |
+
"score_details": {
|
| 326 |
+
"score": 0.817
|
| 327 |
+
}
|
| 328 |
+
},
|
| 329 |
+
{
|
| 330 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 331 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 332 |
+
"source_data": {
|
| 333 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 334 |
+
"source_type": "hf_dataset",
|
| 335 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 336 |
+
"hf_split": "train",
|
| 337 |
+
"additional_details": {
|
| 338 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 339 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 340 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 342 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 343 |
+
"dataset_total_questions": "12000",
|
| 344 |
+
"prompt_style": "5-shot CoT"
|
| 345 |
+
}
|
| 346 |
+
},
|
| 347 |
+
"metric_config": {
|
| 348 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 349 |
+
"metric_id": "mmlu_pro/history",
|
| 350 |
+
"metric_name": "MMLU-Pro (History)",
|
| 351 |
+
"metric_kind": "accuracy",
|
| 352 |
+
"metric_unit": "proportion",
|
| 353 |
+
"lower_is_better": false,
|
| 354 |
+
"score_type": "continuous",
|
| 355 |
+
"min_score": 0.0,
|
| 356 |
+
"max_score": 1.0,
|
| 357 |
+
"additional_details": {
|
| 358 |
+
"aggregation": "accuracy_over_subset",
|
| 359 |
+
"prompt_style": "5-shot CoT"
|
| 360 |
+
}
|
| 361 |
+
},
|
| 362 |
+
"score_details": {
|
| 363 |
+
"score": 0.7553
|
| 364 |
+
}
|
| 365 |
+
},
|
| 366 |
+
{
|
| 367 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 368 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 369 |
+
"source_data": {
|
| 370 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 371 |
+
"source_type": "hf_dataset",
|
| 372 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 373 |
+
"hf_split": "train",
|
| 374 |
+
"additional_details": {
|
| 375 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 376 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 377 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 379 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 380 |
+
"dataset_total_questions": "12000",
|
| 381 |
+
"prompt_style": "5-shot CoT"
|
| 382 |
+
}
|
| 383 |
+
},
|
| 384 |
+
"metric_config": {
|
| 385 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 386 |
+
"metric_id": "mmlu_pro/law",
|
| 387 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 388 |
+
"metric_kind": "accuracy",
|
| 389 |
+
"metric_unit": "proportion",
|
| 390 |
+
"lower_is_better": false,
|
| 391 |
+
"score_type": "continuous",
|
| 392 |
+
"min_score": 0.0,
|
| 393 |
+
"max_score": 1.0,
|
| 394 |
+
"additional_details": {
|
| 395 |
+
"aggregation": "accuracy_over_subset",
|
| 396 |
+
"prompt_style": "5-shot CoT"
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"score_details": {
|
| 400 |
+
"score": 0.7244
|
| 401 |
+
}
|
| 402 |
+
},
|
| 403 |
+
{
|
| 404 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 405 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 406 |
+
"source_data": {
|
| 407 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 408 |
+
"source_type": "hf_dataset",
|
| 409 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 410 |
+
"hf_split": "train",
|
| 411 |
+
"additional_details": {
|
| 412 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 413 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 414 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 416 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 417 |
+
"dataset_total_questions": "12000",
|
| 418 |
+
"prompt_style": "5-shot CoT"
|
| 419 |
+
}
|
| 420 |
+
},
|
| 421 |
+
"metric_config": {
|
| 422 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 423 |
+
"metric_id": "mmlu_pro/math",
|
| 424 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 425 |
+
"metric_kind": "accuracy",
|
| 426 |
+
"metric_unit": "proportion",
|
| 427 |
+
"lower_is_better": false,
|
| 428 |
+
"score_type": "continuous",
|
| 429 |
+
"min_score": 0.0,
|
| 430 |
+
"max_score": 1.0,
|
| 431 |
+
"additional_details": {
|
| 432 |
+
"aggregation": "accuracy_over_subset",
|
| 433 |
+
"prompt_style": "5-shot CoT"
|
| 434 |
+
}
|
| 435 |
+
},
|
| 436 |
+
"score_details": {
|
| 437 |
+
"score": 0.8884
|
| 438 |
+
}
|
| 439 |
+
},
|
| 440 |
+
{
|
| 441 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 442 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 443 |
+
"source_data": {
|
| 444 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 445 |
+
"source_type": "hf_dataset",
|
| 446 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 447 |
+
"hf_split": "train",
|
| 448 |
+
"additional_details": {
|
| 449 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 450 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 451 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 453 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 454 |
+
"dataset_total_questions": "12000",
|
| 455 |
+
"prompt_style": "5-shot CoT"
|
| 456 |
+
}
|
| 457 |
+
},
|
| 458 |
+
"metric_config": {
|
| 459 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 460 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 461 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 462 |
+
"metric_kind": "accuracy",
|
| 463 |
+
"metric_unit": "proportion",
|
| 464 |
+
"lower_is_better": false,
|
| 465 |
+
"score_type": "continuous",
|
| 466 |
+
"min_score": 0.0,
|
| 467 |
+
"max_score": 1.0,
|
| 468 |
+
"additional_details": {
|
| 469 |
+
"aggregation": "accuracy_over_subset",
|
| 470 |
+
"prompt_style": "5-shot CoT"
|
| 471 |
+
}
|
| 472 |
+
},
|
| 473 |
+
"score_details": {
|
| 474 |
+
"score": 0.8327
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 479 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 480 |
+
"source_data": {
|
| 481 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 482 |
+
"source_type": "hf_dataset",
|
| 483 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 484 |
+
"hf_split": "train",
|
| 485 |
+
"additional_details": {
|
| 486 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 487 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 488 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 490 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 491 |
+
"dataset_total_questions": "12000",
|
| 492 |
+
"prompt_style": "5-shot CoT"
|
| 493 |
+
}
|
| 494 |
+
},
|
| 495 |
+
"metric_config": {
|
| 496 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 497 |
+
"metric_id": "mmlu_pro/physics",
|
| 498 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 499 |
+
"metric_kind": "accuracy",
|
| 500 |
+
"metric_unit": "proportion",
|
| 501 |
+
"lower_is_better": false,
|
| 502 |
+
"score_type": "continuous",
|
| 503 |
+
"min_score": 0.0,
|
| 504 |
+
"max_score": 1.0,
|
| 505 |
+
"additional_details": {
|
| 506 |
+
"aggregation": "accuracy_over_subset",
|
| 507 |
+
"prompt_style": "5-shot CoT"
|
| 508 |
+
}
|
| 509 |
+
},
|
| 510 |
+
"score_details": {
|
| 511 |
+
"score": 0.8831
|
| 512 |
+
}
|
| 513 |
+
},
|
| 514 |
+
{
|
| 515 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 516 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 517 |
+
"source_data": {
|
| 518 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 519 |
+
"source_type": "hf_dataset",
|
| 520 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 521 |
+
"hf_split": "train",
|
| 522 |
+
"additional_details": {
|
| 523 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 524 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 525 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 527 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 528 |
+
"dataset_total_questions": "12000",
|
| 529 |
+
"prompt_style": "5-shot CoT"
|
| 530 |
+
}
|
| 531 |
+
},
|
| 532 |
+
"metric_config": {
|
| 533 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 534 |
+
"metric_id": "mmlu_pro/psychology",
|
| 535 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 536 |
+
"metric_kind": "accuracy",
|
| 537 |
+
"metric_unit": "proportion",
|
| 538 |
+
"lower_is_better": false,
|
| 539 |
+
"score_type": "continuous",
|
| 540 |
+
"min_score": 0.0,
|
| 541 |
+
"max_score": 1.0,
|
| 542 |
+
"additional_details": {
|
| 543 |
+
"aggregation": "accuracy_over_subset",
|
| 544 |
+
"prompt_style": "5-shot CoT"
|
| 545 |
+
}
|
| 546 |
+
},
|
| 547 |
+
"score_details": {
|
| 548 |
+
"score": 0.8726
|
| 549 |
+
}
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 553 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 554 |
+
"source_data": {
|
| 555 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 556 |
+
"source_type": "hf_dataset",
|
| 557 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 558 |
+
"hf_split": "train",
|
| 559 |
+
"additional_details": {
|
| 560 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 561 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 562 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 564 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 565 |
+
"dataset_total_questions": "12000",
|
| 566 |
+
"prompt_style": "5-shot CoT"
|
| 567 |
+
}
|
| 568 |
+
},
|
| 569 |
+
"metric_config": {
|
| 570 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 571 |
+
"metric_id": "mmlu_pro/other",
|
| 572 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 573 |
+
"metric_kind": "accuracy",
|
| 574 |
+
"metric_unit": "proportion",
|
| 575 |
+
"lower_is_better": false,
|
| 576 |
+
"score_type": "continuous",
|
| 577 |
+
"min_score": 0.0,
|
| 578 |
+
"max_score": 1.0,
|
| 579 |
+
"additional_details": {
|
| 580 |
+
"aggregation": "accuracy_over_subset",
|
| 581 |
+
"prompt_style": "5-shot CoT"
|
| 582 |
+
}
|
| 583 |
+
},
|
| 584 |
+
"score_details": {
|
| 585 |
+
"score": 0.8295
|
| 586 |
+
}
|
| 587 |
+
}
|
| 588 |
+
]
|
| 589 |
+
}
|
data/mmlu_pro/google/gemini-2.5-pro/4ac5ed20-3428-462d-b108-9d4bfb2fed2b.json
ADDED
|
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-2.5-pro/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-2.5-Pro",
|
| 25 |
+
"id": "google/gemini-2.5-pro",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-2.5-Pro",
|
| 29 |
+
"leaderboard_data_source": "Self-Reported"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.86
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
]
|
| 71 |
+
}
|
data/mmlu_pro/google/gemini-3.1-flash-lite-preview/9e560681-ebd4-4d96-92fa-ef8e8e6c64fe.json
ADDED
|
@@ -0,0 +1,71 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-3.1-flash-lite-preview/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-3.1-Flash-Lite-Preview",
|
| 25 |
+
"id": "google/gemini-3.1-flash-lite-preview",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-3.1-Flash-Lite-Preview",
|
| 29 |
+
"leaderboard_data_source": "Self-Reported"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.862
|
| 68 |
+
}
|
| 69 |
+
}
|
| 70 |
+
]
|
| 71 |
+
}
|
data/mmlu_pro/google/gemini-3.1-pro/c03b9f04-fa5b-41e8-b58f-0bfe85be3c0f.json
ADDED
|
@@ -0,0 +1,589 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemini-3.1-pro/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemini-3.1-Pro",
|
| 25 |
+
"id": "google/gemini-3.1-pro",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemini-3.1-Pro",
|
| 29 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 30 |
+
}
|
| 31 |
+
},
|
| 32 |
+
"evaluation_results": [
|
| 33 |
+
{
|
| 34 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 35 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 36 |
+
"source_data": {
|
| 37 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 38 |
+
"source_type": "hf_dataset",
|
| 39 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 40 |
+
"hf_split": "train",
|
| 41 |
+
"additional_details": {
|
| 42 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 43 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 44 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 46 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 47 |
+
"dataset_total_questions": "12000",
|
| 48 |
+
"prompt_style": "5-shot CoT"
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"metric_config": {
|
| 52 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 53 |
+
"metric_id": "mmlu_pro/overall",
|
| 54 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 55 |
+
"metric_kind": "accuracy",
|
| 56 |
+
"metric_unit": "proportion",
|
| 57 |
+
"lower_is_better": false,
|
| 58 |
+
"score_type": "continuous",
|
| 59 |
+
"min_score": 0.0,
|
| 60 |
+
"max_score": 1.0,
|
| 61 |
+
"additional_details": {
|
| 62 |
+
"aggregation": "accuracy_over_subset",
|
| 63 |
+
"prompt_style": "5-shot CoT"
|
| 64 |
+
}
|
| 65 |
+
},
|
| 66 |
+
"score_details": {
|
| 67 |
+
"score": 0.9116
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
{
|
| 71 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 72 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 73 |
+
"source_data": {
|
| 74 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 75 |
+
"source_type": "hf_dataset",
|
| 76 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 77 |
+
"hf_split": "train",
|
| 78 |
+
"additional_details": {
|
| 79 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 80 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 81 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 83 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 84 |
+
"dataset_total_questions": "12000",
|
| 85 |
+
"prompt_style": "5-shot CoT"
|
| 86 |
+
}
|
| 87 |
+
},
|
| 88 |
+
"metric_config": {
|
| 89 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 90 |
+
"metric_id": "mmlu_pro/biology",
|
| 91 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 92 |
+
"metric_kind": "accuracy",
|
| 93 |
+
"metric_unit": "proportion",
|
| 94 |
+
"lower_is_better": false,
|
| 95 |
+
"score_type": "continuous",
|
| 96 |
+
"min_score": 0.0,
|
| 97 |
+
"max_score": 1.0,
|
| 98 |
+
"additional_details": {
|
| 99 |
+
"aggregation": "accuracy_over_subset",
|
| 100 |
+
"prompt_style": "5-shot CoT"
|
| 101 |
+
}
|
| 102 |
+
},
|
| 103 |
+
"score_details": {
|
| 104 |
+
"score": 0.9582
|
| 105 |
+
}
|
| 106 |
+
},
|
| 107 |
+
{
|
| 108 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 109 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 110 |
+
"source_data": {
|
| 111 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 112 |
+
"source_type": "hf_dataset",
|
| 113 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 114 |
+
"hf_split": "train",
|
| 115 |
+
"additional_details": {
|
| 116 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 117 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 118 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 120 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 121 |
+
"dataset_total_questions": "12000",
|
| 122 |
+
"prompt_style": "5-shot CoT"
|
| 123 |
+
}
|
| 124 |
+
},
|
| 125 |
+
"metric_config": {
|
| 126 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 127 |
+
"metric_id": "mmlu_pro/business",
|
| 128 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 129 |
+
"metric_kind": "accuracy",
|
| 130 |
+
"metric_unit": "proportion",
|
| 131 |
+
"lower_is_better": false,
|
| 132 |
+
"score_type": "continuous",
|
| 133 |
+
"min_score": 0.0,
|
| 134 |
+
"max_score": 1.0,
|
| 135 |
+
"additional_details": {
|
| 136 |
+
"aggregation": "accuracy_over_subset",
|
| 137 |
+
"prompt_style": "5-shot CoT"
|
| 138 |
+
}
|
| 139 |
+
},
|
| 140 |
+
"score_details": {
|
| 141 |
+
"score": 0.9328
|
| 142 |
+
}
|
| 143 |
+
},
|
| 144 |
+
{
|
| 145 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 146 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 147 |
+
"source_data": {
|
| 148 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 149 |
+
"source_type": "hf_dataset",
|
| 150 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 151 |
+
"hf_split": "train",
|
| 152 |
+
"additional_details": {
|
| 153 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 154 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 155 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 157 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 158 |
+
"dataset_total_questions": "12000",
|
| 159 |
+
"prompt_style": "5-shot CoT"
|
| 160 |
+
}
|
| 161 |
+
},
|
| 162 |
+
"metric_config": {
|
| 163 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 164 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 165 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 166 |
+
"metric_kind": "accuracy",
|
| 167 |
+
"metric_unit": "proportion",
|
| 168 |
+
"lower_is_better": false,
|
| 169 |
+
"score_type": "continuous",
|
| 170 |
+
"min_score": 0.0,
|
| 171 |
+
"max_score": 1.0,
|
| 172 |
+
"additional_details": {
|
| 173 |
+
"aggregation": "accuracy_over_subset",
|
| 174 |
+
"prompt_style": "5-shot CoT"
|
| 175 |
+
}
|
| 176 |
+
},
|
| 177 |
+
"score_details": {
|
| 178 |
+
"score": 0.9214
|
| 179 |
+
}
|
| 180 |
+
},
|
| 181 |
+
{
|
| 182 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 183 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 184 |
+
"source_data": {
|
| 185 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 186 |
+
"source_type": "hf_dataset",
|
| 187 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 188 |
+
"hf_split": "train",
|
| 189 |
+
"additional_details": {
|
| 190 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 191 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 192 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 194 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 195 |
+
"dataset_total_questions": "12000",
|
| 196 |
+
"prompt_style": "5-shot CoT"
|
| 197 |
+
}
|
| 198 |
+
},
|
| 199 |
+
"metric_config": {
|
| 200 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 201 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 202 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 203 |
+
"metric_kind": "accuracy",
|
| 204 |
+
"metric_unit": "proportion",
|
| 205 |
+
"lower_is_better": false,
|
| 206 |
+
"score_type": "continuous",
|
| 207 |
+
"min_score": 0.0,
|
| 208 |
+
"max_score": 1.0,
|
| 209 |
+
"additional_details": {
|
| 210 |
+
"aggregation": "accuracy_over_subset",
|
| 211 |
+
"prompt_style": "5-shot CoT"
|
| 212 |
+
}
|
| 213 |
+
},
|
| 214 |
+
"score_details": {
|
| 215 |
+
"score": 0.9366
|
| 216 |
+
}
|
| 217 |
+
},
|
| 218 |
+
{
|
| 219 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 220 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 221 |
+
"source_data": {
|
| 222 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 223 |
+
"source_type": "hf_dataset",
|
| 224 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 225 |
+
"hf_split": "train",
|
| 226 |
+
"additional_details": {
|
| 227 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 228 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 229 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 231 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 232 |
+
"dataset_total_questions": "12000",
|
| 233 |
+
"prompt_style": "5-shot CoT"
|
| 234 |
+
}
|
| 235 |
+
},
|
| 236 |
+
"metric_config": {
|
| 237 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 238 |
+
"metric_id": "mmlu_pro/economics",
|
| 239 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 240 |
+
"metric_kind": "accuracy",
|
| 241 |
+
"metric_unit": "proportion",
|
| 242 |
+
"lower_is_better": false,
|
| 243 |
+
"score_type": "continuous",
|
| 244 |
+
"min_score": 0.0,
|
| 245 |
+
"max_score": 1.0,
|
| 246 |
+
"additional_details": {
|
| 247 |
+
"aggregation": "accuracy_over_subset",
|
| 248 |
+
"prompt_style": "5-shot CoT"
|
| 249 |
+
}
|
| 250 |
+
},
|
| 251 |
+
"score_details": {
|
| 252 |
+
"score": 0.9336
|
| 253 |
+
}
|
| 254 |
+
},
|
| 255 |
+
{
|
| 256 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 257 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 258 |
+
"source_data": {
|
| 259 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 260 |
+
"source_type": "hf_dataset",
|
| 261 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 262 |
+
"hf_split": "train",
|
| 263 |
+
"additional_details": {
|
| 264 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 265 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 266 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 268 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 269 |
+
"dataset_total_questions": "12000",
|
| 270 |
+
"prompt_style": "5-shot CoT"
|
| 271 |
+
}
|
| 272 |
+
},
|
| 273 |
+
"metric_config": {
|
| 274 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 275 |
+
"metric_id": "mmlu_pro/engineering",
|
| 276 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 277 |
+
"metric_kind": "accuracy",
|
| 278 |
+
"metric_unit": "proportion",
|
| 279 |
+
"lower_is_better": false,
|
| 280 |
+
"score_type": "continuous",
|
| 281 |
+
"min_score": 0.0,
|
| 282 |
+
"max_score": 1.0,
|
| 283 |
+
"additional_details": {
|
| 284 |
+
"aggregation": "accuracy_over_subset",
|
| 285 |
+
"prompt_style": "5-shot CoT"
|
| 286 |
+
}
|
| 287 |
+
},
|
| 288 |
+
"score_details": {
|
| 289 |
+
"score": 0.8733
|
| 290 |
+
}
|
| 291 |
+
},
|
| 292 |
+
{
|
| 293 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 294 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 295 |
+
"source_data": {
|
| 296 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 297 |
+
"source_type": "hf_dataset",
|
| 298 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 299 |
+
"hf_split": "train",
|
| 300 |
+
"additional_details": {
|
| 301 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 302 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 303 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 305 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 306 |
+
"dataset_total_questions": "12000",
|
| 307 |
+
"prompt_style": "5-shot CoT"
|
| 308 |
+
}
|
| 309 |
+
},
|
| 310 |
+
"metric_config": {
|
| 311 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 312 |
+
"metric_id": "mmlu_pro/health",
|
| 313 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 314 |
+
"metric_kind": "accuracy",
|
| 315 |
+
"metric_unit": "proportion",
|
| 316 |
+
"lower_is_better": false,
|
| 317 |
+
"score_type": "continuous",
|
| 318 |
+
"min_score": 0.0,
|
| 319 |
+
"max_score": 1.0,
|
| 320 |
+
"additional_details": {
|
| 321 |
+
"aggregation": "accuracy_over_subset",
|
| 322 |
+
"prompt_style": "5-shot CoT"
|
| 323 |
+
}
|
| 324 |
+
},
|
| 325 |
+
"score_details": {
|
| 326 |
+
"score": 0.8619
|
| 327 |
+
}
|
| 328 |
+
},
|
| 329 |
+
{
|
| 330 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 331 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 332 |
+
"source_data": {
|
| 333 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 334 |
+
"source_type": "hf_dataset",
|
| 335 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 336 |
+
"hf_split": "train",
|
| 337 |
+
"additional_details": {
|
| 338 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 339 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 340 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 342 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 343 |
+
"dataset_total_questions": "12000",
|
| 344 |
+
"prompt_style": "5-shot CoT"
|
| 345 |
+
}
|
| 346 |
+
},
|
| 347 |
+
"metric_config": {
|
| 348 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 349 |
+
"metric_id": "mmlu_pro/history",
|
| 350 |
+
"metric_name": "MMLU-Pro (History)",
|
| 351 |
+
"metric_kind": "accuracy",
|
| 352 |
+
"metric_unit": "proportion",
|
| 353 |
+
"lower_is_better": false,
|
| 354 |
+
"score_type": "continuous",
|
| 355 |
+
"min_score": 0.0,
|
| 356 |
+
"max_score": 1.0,
|
| 357 |
+
"additional_details": {
|
| 358 |
+
"aggregation": "accuracy_over_subset",
|
| 359 |
+
"prompt_style": "5-shot CoT"
|
| 360 |
+
}
|
| 361 |
+
},
|
| 362 |
+
"score_details": {
|
| 363 |
+
"score": 0.8556
|
| 364 |
+
}
|
| 365 |
+
},
|
| 366 |
+
{
|
| 367 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 368 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 369 |
+
"source_data": {
|
| 370 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 371 |
+
"source_type": "hf_dataset",
|
| 372 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 373 |
+
"hf_split": "train",
|
| 374 |
+
"additional_details": {
|
| 375 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 376 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 377 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 379 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 380 |
+
"dataset_total_questions": "12000",
|
| 381 |
+
"prompt_style": "5-shot CoT"
|
| 382 |
+
}
|
| 383 |
+
},
|
| 384 |
+
"metric_config": {
|
| 385 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 386 |
+
"metric_id": "mmlu_pro/law",
|
| 387 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 388 |
+
"metric_kind": "accuracy",
|
| 389 |
+
"metric_unit": "proportion",
|
| 390 |
+
"lower_is_better": false,
|
| 391 |
+
"score_type": "continuous",
|
| 392 |
+
"min_score": 0.0,
|
| 393 |
+
"max_score": 1.0,
|
| 394 |
+
"additional_details": {
|
| 395 |
+
"aggregation": "accuracy_over_subset",
|
| 396 |
+
"prompt_style": "5-shot CoT"
|
| 397 |
+
}
|
| 398 |
+
},
|
| 399 |
+
"score_details": {
|
| 400 |
+
"score": 0.8564
|
| 401 |
+
}
|
| 402 |
+
},
|
| 403 |
+
{
|
| 404 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 405 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 406 |
+
"source_data": {
|
| 407 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 408 |
+
"source_type": "hf_dataset",
|
| 409 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 410 |
+
"hf_split": "train",
|
| 411 |
+
"additional_details": {
|
| 412 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 413 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 414 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 416 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 417 |
+
"dataset_total_questions": "12000",
|
| 418 |
+
"prompt_style": "5-shot CoT"
|
| 419 |
+
}
|
| 420 |
+
},
|
| 421 |
+
"metric_config": {
|
| 422 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 423 |
+
"metric_id": "mmlu_pro/math",
|
| 424 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 425 |
+
"metric_kind": "accuracy",
|
| 426 |
+
"metric_unit": "proportion",
|
| 427 |
+
"lower_is_better": false,
|
| 428 |
+
"score_type": "continuous",
|
| 429 |
+
"min_score": 0.0,
|
| 430 |
+
"max_score": 1.0,
|
| 431 |
+
"additional_details": {
|
| 432 |
+
"aggregation": "accuracy_over_subset",
|
| 433 |
+
"prompt_style": "5-shot CoT"
|
| 434 |
+
}
|
| 435 |
+
},
|
| 436 |
+
"score_details": {
|
| 437 |
+
"score": 0.9548
|
| 438 |
+
}
|
| 439 |
+
},
|
| 440 |
+
{
|
| 441 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 442 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 443 |
+
"source_data": {
|
| 444 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 445 |
+
"source_type": "hf_dataset",
|
| 446 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 447 |
+
"hf_split": "train",
|
| 448 |
+
"additional_details": {
|
| 449 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 450 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 451 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 453 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 454 |
+
"dataset_total_questions": "12000",
|
| 455 |
+
"prompt_style": "5-shot CoT"
|
| 456 |
+
}
|
| 457 |
+
},
|
| 458 |
+
"metric_config": {
|
| 459 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 460 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 461 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 462 |
+
"metric_kind": "accuracy",
|
| 463 |
+
"metric_unit": "proportion",
|
| 464 |
+
"lower_is_better": false,
|
| 465 |
+
"score_type": "continuous",
|
| 466 |
+
"min_score": 0.0,
|
| 467 |
+
"max_score": 1.0,
|
| 468 |
+
"additional_details": {
|
| 469 |
+
"aggregation": "accuracy_over_subset",
|
| 470 |
+
"prompt_style": "5-shot CoT"
|
| 471 |
+
}
|
| 472 |
+
},
|
| 473 |
+
"score_details": {
|
| 474 |
+
"score": 0.9038
|
| 475 |
+
}
|
| 476 |
+
},
|
| 477 |
+
{
|
| 478 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 479 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 480 |
+
"source_data": {
|
| 481 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 482 |
+
"source_type": "hf_dataset",
|
| 483 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 484 |
+
"hf_split": "train",
|
| 485 |
+
"additional_details": {
|
| 486 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 487 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 488 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 490 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 491 |
+
"dataset_total_questions": "12000",
|
| 492 |
+
"prompt_style": "5-shot CoT"
|
| 493 |
+
}
|
| 494 |
+
},
|
| 495 |
+
"metric_config": {
|
| 496 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 497 |
+
"metric_id": "mmlu_pro/physics",
|
| 498 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 499 |
+
"metric_kind": "accuracy",
|
| 500 |
+
"metric_unit": "proportion",
|
| 501 |
+
"lower_is_better": false,
|
| 502 |
+
"score_type": "continuous",
|
| 503 |
+
"min_score": 0.0,
|
| 504 |
+
"max_score": 1.0,
|
| 505 |
+
"additional_details": {
|
| 506 |
+
"aggregation": "accuracy_over_subset",
|
| 507 |
+
"prompt_style": "5-shot CoT"
|
| 508 |
+
}
|
| 509 |
+
},
|
| 510 |
+
"score_details": {
|
| 511 |
+
"score": 0.932
|
| 512 |
+
}
|
| 513 |
+
},
|
| 514 |
+
{
|
| 515 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 516 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 517 |
+
"source_data": {
|
| 518 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 519 |
+
"source_type": "hf_dataset",
|
| 520 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 521 |
+
"hf_split": "train",
|
| 522 |
+
"additional_details": {
|
| 523 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 524 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 525 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 527 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 528 |
+
"dataset_total_questions": "12000",
|
| 529 |
+
"prompt_style": "5-shot CoT"
|
| 530 |
+
}
|
| 531 |
+
},
|
| 532 |
+
"metric_config": {
|
| 533 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 534 |
+
"metric_id": "mmlu_pro/psychology",
|
| 535 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 536 |
+
"metric_kind": "accuracy",
|
| 537 |
+
"metric_unit": "proportion",
|
| 538 |
+
"lower_is_better": false,
|
| 539 |
+
"score_type": "continuous",
|
| 540 |
+
"min_score": 0.0,
|
| 541 |
+
"max_score": 1.0,
|
| 542 |
+
"additional_details": {
|
| 543 |
+
"aggregation": "accuracy_over_subset",
|
| 544 |
+
"prompt_style": "5-shot CoT"
|
| 545 |
+
}
|
| 546 |
+
},
|
| 547 |
+
"score_details": {
|
| 548 |
+
"score": 0.9185
|
| 549 |
+
}
|
| 550 |
+
},
|
| 551 |
+
{
|
| 552 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 553 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 554 |
+
"source_data": {
|
| 555 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 556 |
+
"source_type": "hf_dataset",
|
| 557 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 558 |
+
"hf_split": "train",
|
| 559 |
+
"additional_details": {
|
| 560 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 561 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 562 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 564 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 565 |
+
"dataset_total_questions": "12000",
|
| 566 |
+
"prompt_style": "5-shot CoT"
|
| 567 |
+
}
|
| 568 |
+
},
|
| 569 |
+
"metric_config": {
|
| 570 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 571 |
+
"metric_id": "mmlu_pro/other",
|
| 572 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 573 |
+
"metric_kind": "accuracy",
|
| 574 |
+
"metric_unit": "proportion",
|
| 575 |
+
"lower_is_better": false,
|
| 576 |
+
"score_type": "continuous",
|
| 577 |
+
"min_score": 0.0,
|
| 578 |
+
"max_score": 1.0,
|
| 579 |
+
"additional_details": {
|
| 580 |
+
"aggregation": "accuracy_over_subset",
|
| 581 |
+
"prompt_style": "5-shot CoT"
|
| 582 |
+
}
|
| 583 |
+
},
|
| 584 |
+
"score_details": {
|
| 585 |
+
"score": 0.8929
|
| 586 |
+
}
|
| 587 |
+
}
|
| 588 |
+
]
|
| 589 |
+
}
|
data/mmlu_pro/google/gemma-2-27b-it/e8295867-8053-488f-9130-9eb99b97670e.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-2-27b-it/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-2-27B-it",
|
| 25 |
+
"id": "google/gemma-2-27b-it",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-2-27B-it",
|
| 29 |
+
"size_billions_parameters": "27.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.5654
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.7796
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.6008
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.5371
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.5683
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.6979
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3488
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.6186
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.5722
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.3951
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.5611
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.523
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.5296
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.7155
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.6115
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/google/gemma-2-2b-it/7461516d-9358-42db-9888-056fd84c525f.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-2-2b-it/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-2-2B-it",
|
| 25 |
+
"id": "google/gemma-2-2b-it",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-2-2B-it",
|
| 29 |
+
"size_billions_parameters": "2.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.156
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/google/gemma-2-9b-it/c9fcb231-ddb3-4c0a-8d57-3a3782f0d752.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-2-9b-it/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-2-9B-it",
|
| 25 |
+
"id": "google/gemma-2-9b-it",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-2-9B-it",
|
| 29 |
+
"size_billions_parameters": "9.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.5208
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.7587
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.5539
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.4664
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.5073
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.6552
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3622
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.5844
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.5354
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.3579
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.4944
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.495
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.4758
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.6617
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.5498
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/google/gemma-2-9b/9c2c6d87-805f-47c0-9ddf-e0b81d0f69f8.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-2-9b/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-2-9B",
|
| 25 |
+
"id": "google/gemma-2-9b",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-2-9B",
|
| 29 |
+
"size_billions_parameters": "9.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.451
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.6457
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.4284
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.3746
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.4122
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.5486
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3075
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.5232
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.4987
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2843
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.4041
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.485
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.4296
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.6353
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.5271
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/google/gemma-2b/eb556f91-0cbd-4530-a7d1-d80e86d42588.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-2b/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-2B",
|
| 25 |
+
"id": "google/gemma-2b",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-2B",
|
| 29 |
+
"size_billions_parameters": "2.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.1585
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.2482
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.1457
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1378
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.1414
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.1753
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.1269
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.177
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.154
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.123
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.163
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.1482
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.1563
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.1608
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.1817
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/google/gemma-3-12b-it/83198456-6a19-4574-abf8-0a0cc97287af.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-3-12b-it/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-3-12B-it",
|
| 25 |
+
"id": "google/gemma-3-12b-it",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-3-12B-it",
|
| 29 |
+
"size_billions_parameters": "12.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.606
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/google/gemma-3-1b-it/e3c1d8e7-a98f-4fd4-ac24-3d0c7c3669a9.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-3-1b-it/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-3-1B-it",
|
| 25 |
+
"id": "google/gemma-3-1b-it",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-3-1B-it",
|
| 29 |
+
"size_billions_parameters": "1.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.147
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/google/gemma-3-27b-it/6a53d694-91e0-40bd-a265-1f640756ae66.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-3-27b-it/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-3-27B-it",
|
| 25 |
+
"id": "google/gemma-3-27b-it",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-3-27B-it",
|
| 29 |
+
"size_billions_parameters": "27.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.675
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/google/gemma-3-4b-it/e7c8cf64-4f7a-4a14-8517-00760409be7c.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-3-4b-it/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-3-4B-it",
|
| 25 |
+
"id": "google/gemma-3-4b-it",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-3-4B-it",
|
| 29 |
+
"size_billions_parameters": "4.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.436
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/google/gemma-7b/1e21b40e-8ae9-4712-87ad-344e5423846b.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/google_gemma-7b/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Gemma-7B",
|
| 25 |
+
"id": "google/gemma-7b",
|
| 26 |
+
"developer": "google",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Gemma-7B",
|
| 29 |
+
"size_billions_parameters": "7.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.3373
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.5649
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.3333
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.2624
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.3659
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.4242
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.227
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.3716
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.3675
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2171
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.2509
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.3908
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.2756
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.5175
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.4091
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.0-2b-base/47ec1fc0-0ac3-4d86-87f6-274667ef2a16.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.0-2b-base/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.0-2B-Base",
|
| 25 |
+
"id": "ibm/granite-3.0-2b-base",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.0-2B-Base",
|
| 29 |
+
"size_billions_parameters": "2.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.2172
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.3445
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.1977
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1564
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.2659
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.3033
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.1465
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.2298
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.1837
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.1653
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.2058
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.2385
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.164
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.3271
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.2327
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.0-8b-base/5e811a7a-5a2c-4484-9368-5d62dab1f13f.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.0-8b-base/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.0-8B-Base",
|
| 25 |
+
"id": "ibm/granite-3.0-8b-base",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.0-8B-Base",
|
| 29 |
+
"size_billions_parameters": "8.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.3103
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.4728
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.308
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.2217
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.3268
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.4135
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.2136
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.3863
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.3307
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2316
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.2805
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.3467
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.2494
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.4336
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.3149
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.1-1b-a400m-base/1f8d80d4-01a3-4818-9a8b-d00054dcab43.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.1-1b-a400m-base/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.1-1B-A400M-Base",
|
| 25 |
+
"id": "ibm/granite-3.1-1b-a400m-base",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.1-1B-A400M-Base",
|
| 29 |
+
"size_billions_parameters": "1.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.1234
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.1353
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.1153
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1246
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.1415
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.1422
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.098
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.1308
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.1234
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.1126
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.131
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.1683
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.1124
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.1078
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.1212
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.1-1b-a400m-instruct/f3eeed4c-1d73-46f0-acac-eafd5fc9754f.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.1-1b-a400m-instruct/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.1-1B-A400M-Instruct",
|
| 25 |
+
"id": "ibm/granite-3.1-1b-a400m-instruct",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.1-1B-A400M-Instruct",
|
| 29 |
+
"size_billions_parameters": "1.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.1327
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.1437
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.1267
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1148
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.161
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.1576
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.1125
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.1638
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.1129
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.1253
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.1303
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.1202
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.1209
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.1617
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.1288
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.1-2b-base/ba525354-6d57-4b26-b777-e58271890ae6.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.1-2b-base/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.1-2B-Base",
|
| 25 |
+
"id": "ibm/granite-3.1-2b-base",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.1-2B-Base",
|
| 29 |
+
"size_billions_parameters": "2.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.2389
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.3752
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.256
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1696
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.2439
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.3092
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.193
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.2604
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.2178
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.1253
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.2487
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.2525
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.1986
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.3421
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.2565
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.1-2b-instruct/7959f684-3532-439e-a9e0-5389dcaf795c.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.1-2b-instruct/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.1-2B-Instruct",
|
| 25 |
+
"id": "ibm/granite-3.1-2b-instruct",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.1-2B-Instruct",
|
| 29 |
+
"size_billions_parameters": "2.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.3197
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.5007
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.3308
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.2412
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.3707
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.4111
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.2528
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.3056
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.3045
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.218
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.3442
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.2846
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.2648
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.4411
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.3258
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.1-3b-a800m-base/95d5b3bc-f7e5-4171-9be8-9656a902d92f.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.1-3b-a800m-base/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.1-3B-A800M-Base",
|
| 25 |
+
"id": "ibm/granite-3.1-3b-a800m-base",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.1-3B-A800M-Base",
|
| 29 |
+
"size_billions_parameters": "3.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.2039
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.2957
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.1762
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1405
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.2268
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.2737
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.1527
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.2286
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.1995
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.1444
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.2198
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.2305
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.164
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.3083
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.1926
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.1-3b-a800m-instruct/b186c499-e9e3-4fef-8bbf-3c43f7c71e28.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.1-3b-a800m-instruct/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.1-3B-A800M-Instruct",
|
| 25 |
+
"id": "ibm/granite-3.1-3b-a800m-instruct",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.1-3B-A800M-Instruct",
|
| 29 |
+
"size_billions_parameters": "3.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.2542
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.3431
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.2725
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.1608
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.2829
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.3626
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.1744
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.2641
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.2415
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.1708
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.2754
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.2806
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.2017
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.381
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.2706
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.1-8b-base/6b91c3fc-4ec1-480c-9e78-99440cbfe97d.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.1-8b-base/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.1-8B-Base",
|
| 25 |
+
"id": "ibm/granite-3.1-8b-base",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.1-8B-Base",
|
| 29 |
+
"size_billions_parameters": "8.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.3308
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.4979
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.3181
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.2403
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.339
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.4372
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.2425
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.3716
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.3412
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2044
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.3249
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.3567
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.2748
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.4862
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.3636
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/ibm/granite-3.1-8b-instruct/07107f2c-5b3e-4b5d-87ec-0acc108a68f9.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/ibm_granite-3.1-8b-instruct/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Granite-3.1-8B-Instruct",
|
| 25 |
+
"id": "ibm/granite-3.1-8b-instruct",
|
| 26 |
+
"developer": "ibm",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Granite-3.1-8B-Instruct",
|
| 29 |
+
"size_billions_parameters": "8.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.4103
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.5746
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.4563
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.3145
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.4244
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.5047
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.291
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.4707
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.4121
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2607
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.4189
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.4329
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.3472
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.5739
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.4405
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/microsoft/phi-3.5-mini-instruct/61ae9c04-3c9a-47e3-bcab-eea1268d14fe.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/microsoft_phi-3.5-mini-instruct/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Phi-3.5-mini-instruct",
|
| 25 |
+
"id": "microsoft/phi-3.5-mini-instruct",
|
| 26 |
+
"developer": "microsoft",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Phi-3.5-mini-instruct",
|
| 29 |
+
"size_billions_parameters": "3.8",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.4787
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.7057
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.5349
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.4125
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.5195
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.6386
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3075
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.5244
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.4252
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.2943
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.49
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.5
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.4509
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.4188
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.6353
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/microsoft/phi-4-mini/86cb1a4f-477f-4c08-8fb4-d0a6153984c3.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/microsoft_phi-4-mini/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Phi-4-mini",
|
| 25 |
+
"id": "microsoft/phi-4-mini",
|
| 26 |
+
"developer": "microsoft",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Phi-4-mini",
|
| 29 |
+
"size_billions_parameters": "5.6",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.528
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/microsoft/phi-4-reasoning-plus/7f6559ff-c8dd-4fe7-9cd4-c1cd34e65c8b.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/microsoft_phi-4-reasoning-plus/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Phi-4-reasoning-plus",
|
| 25 |
+
"id": "microsoft/phi-4-reasoning-plus",
|
| 26 |
+
"developer": "microsoft",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Phi-4-reasoning-plus",
|
| 29 |
+
"size_billions_parameters": "14.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.76
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/microsoft/phi-4-reasoning/a61f15ba-23b9-48d9-bf7c-c1911d765421.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/microsoft_phi-4-reasoning/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Phi-4-reasoning",
|
| 25 |
+
"id": "microsoft/phi-4-reasoning",
|
| 26 |
+
"developer": "microsoft",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Phi-4-reasoning",
|
| 29 |
+
"size_billions_parameters": "14.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.743
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/microsoft/phi-4/c6c805a9-7065-4811-a2ee-aa9b3e065e09.json
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/microsoft_phi-4/self-reported/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "Self-Reported"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Phi-4",
|
| 25 |
+
"id": "microsoft/phi-4",
|
| 26 |
+
"developer": "microsoft",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Phi-4",
|
| 29 |
+
"size_billions_parameters": "14.0",
|
| 30 |
+
"leaderboard_data_source": "Self-Reported"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.704
|
| 69 |
+
}
|
| 70 |
+
}
|
| 71 |
+
]
|
| 72 |
+
}
|
data/mmlu_pro/microsoft/phi3-medium-128k/cfba928a-8592-47f2-bc5d-bb97cd624352.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/microsoft_phi3-medium-128k/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Phi3-medium-128k",
|
| 25 |
+
"id": "microsoft/phi3-medium-128k",
|
| 26 |
+
"developer": "microsoft",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Phi3-medium-128k",
|
| 29 |
+
"size_billions_parameters": "14.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.5191
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.7336
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.564
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.4382
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.5171
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.6647
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3437
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.5856
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.5381
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.3597
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.4989
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.491
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.4519
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.7093
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.5639
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|
data/mmlu_pro/microsoft/phi3-medium-4k/996cc51e-f338-4192-ac74-c3149c097648.json
ADDED
|
@@ -0,0 +1,590 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"schema_version": "0.2.2",
|
| 3 |
+
"evaluation_id": "mmlu-pro/microsoft_phi3-medium-4k/tiger-lab/1783223217.608336",
|
| 4 |
+
"retrieved_timestamp": "1783223217.608336",
|
| 5 |
+
"source_metadata": {
|
| 6 |
+
"source_name": "MMLU-Pro Leaderboard",
|
| 7 |
+
"source_type": "documentation",
|
| 8 |
+
"source_organization_name": "TIGER-Lab",
|
| 9 |
+
"source_organization_url": "https://tiger-ai-lab.github.io",
|
| 10 |
+
"evaluator_relationship": "third_party",
|
| 11 |
+
"additional_details": {
|
| 12 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 13 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 14 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 15 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 16 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 17 |
+
}
|
| 18 |
+
},
|
| 19 |
+
"eval_library": {
|
| 20 |
+
"name": "MMLU-Pro leaderboard (TIGER-Lab)",
|
| 21 |
+
"version": "unknown"
|
| 22 |
+
},
|
| 23 |
+
"model_info": {
|
| 24 |
+
"name": "Phi3-medium-4k",
|
| 25 |
+
"id": "microsoft/phi3-medium-4k",
|
| 26 |
+
"developer": "microsoft",
|
| 27 |
+
"additional_details": {
|
| 28 |
+
"raw_model_name": "Phi3-medium-4k",
|
| 29 |
+
"size_billions_parameters": "14.0",
|
| 30 |
+
"leaderboard_data_source": "TIGER-Lab"
|
| 31 |
+
}
|
| 32 |
+
},
|
| 33 |
+
"evaluation_results": [
|
| 34 |
+
{
|
| 35 |
+
"evaluation_result_id": "mmlu_pro/overall",
|
| 36 |
+
"evaluation_name": "MMLU-Pro (overall)",
|
| 37 |
+
"source_data": {
|
| 38 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 39 |
+
"source_type": "hf_dataset",
|
| 40 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 41 |
+
"hf_split": "train",
|
| 42 |
+
"additional_details": {
|
| 43 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 44 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 45 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 46 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 47 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 48 |
+
"dataset_total_questions": "12000",
|
| 49 |
+
"prompt_style": "5-shot CoT"
|
| 50 |
+
}
|
| 51 |
+
},
|
| 52 |
+
"metric_config": {
|
| 53 |
+
"evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
|
| 54 |
+
"metric_id": "mmlu_pro/overall",
|
| 55 |
+
"metric_name": "MMLU-Pro (overall)",
|
| 56 |
+
"metric_kind": "accuracy",
|
| 57 |
+
"metric_unit": "proportion",
|
| 58 |
+
"lower_is_better": false,
|
| 59 |
+
"score_type": "continuous",
|
| 60 |
+
"min_score": 0.0,
|
| 61 |
+
"max_score": 1.0,
|
| 62 |
+
"additional_details": {
|
| 63 |
+
"aggregation": "accuracy_over_subset",
|
| 64 |
+
"prompt_style": "5-shot CoT"
|
| 65 |
+
}
|
| 66 |
+
},
|
| 67 |
+
"score_details": {
|
| 68 |
+
"score": 0.557
|
| 69 |
+
}
|
| 70 |
+
},
|
| 71 |
+
{
|
| 72 |
+
"evaluation_result_id": "mmlu_pro/biology",
|
| 73 |
+
"evaluation_name": "MMLU-Pro (Biology)",
|
| 74 |
+
"source_data": {
|
| 75 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 76 |
+
"source_type": "hf_dataset",
|
| 77 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 78 |
+
"hf_split": "train",
|
| 79 |
+
"additional_details": {
|
| 80 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 81 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 82 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 83 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 84 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 85 |
+
"dataset_total_questions": "12000",
|
| 86 |
+
"prompt_style": "5-shot CoT"
|
| 87 |
+
}
|
| 88 |
+
},
|
| 89 |
+
"metric_config": {
|
| 90 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
|
| 91 |
+
"metric_id": "mmlu_pro/biology",
|
| 92 |
+
"metric_name": "MMLU-Pro (Biology)",
|
| 93 |
+
"metric_kind": "accuracy",
|
| 94 |
+
"metric_unit": "proportion",
|
| 95 |
+
"lower_is_better": false,
|
| 96 |
+
"score_type": "continuous",
|
| 97 |
+
"min_score": 0.0,
|
| 98 |
+
"max_score": 1.0,
|
| 99 |
+
"additional_details": {
|
| 100 |
+
"aggregation": "accuracy_over_subset",
|
| 101 |
+
"prompt_style": "5-shot CoT"
|
| 102 |
+
}
|
| 103 |
+
},
|
| 104 |
+
"score_details": {
|
| 105 |
+
"score": 0.7587
|
| 106 |
+
}
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"evaluation_result_id": "mmlu_pro/business",
|
| 110 |
+
"evaluation_name": "MMLU-Pro (Business)",
|
| 111 |
+
"source_data": {
|
| 112 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 113 |
+
"source_type": "hf_dataset",
|
| 114 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 115 |
+
"hf_split": "train",
|
| 116 |
+
"additional_details": {
|
| 117 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 118 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 119 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 120 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 121 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 122 |
+
"dataset_total_questions": "12000",
|
| 123 |
+
"prompt_style": "5-shot CoT"
|
| 124 |
+
}
|
| 125 |
+
},
|
| 126 |
+
"metric_config": {
|
| 127 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
|
| 128 |
+
"metric_id": "mmlu_pro/business",
|
| 129 |
+
"metric_name": "MMLU-Pro (Business)",
|
| 130 |
+
"metric_kind": "accuracy",
|
| 131 |
+
"metric_unit": "proportion",
|
| 132 |
+
"lower_is_better": false,
|
| 133 |
+
"score_type": "continuous",
|
| 134 |
+
"min_score": 0.0,
|
| 135 |
+
"max_score": 1.0,
|
| 136 |
+
"additional_details": {
|
| 137 |
+
"aggregation": "accuracy_over_subset",
|
| 138 |
+
"prompt_style": "5-shot CoT"
|
| 139 |
+
}
|
| 140 |
+
},
|
| 141 |
+
"score_details": {
|
| 142 |
+
"score": 0.616
|
| 143 |
+
}
|
| 144 |
+
},
|
| 145 |
+
{
|
| 146 |
+
"evaluation_result_id": "mmlu_pro/chemistry",
|
| 147 |
+
"evaluation_name": "MMLU-Pro (Chemistry)",
|
| 148 |
+
"source_data": {
|
| 149 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 150 |
+
"source_type": "hf_dataset",
|
| 151 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 152 |
+
"hf_split": "train",
|
| 153 |
+
"additional_details": {
|
| 154 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 155 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 156 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 157 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 158 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 159 |
+
"dataset_total_questions": "12000",
|
| 160 |
+
"prompt_style": "5-shot CoT"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"metric_config": {
|
| 164 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
|
| 165 |
+
"metric_id": "mmlu_pro/chemistry",
|
| 166 |
+
"metric_name": "MMLU-Pro (Chemistry)",
|
| 167 |
+
"metric_kind": "accuracy",
|
| 168 |
+
"metric_unit": "proportion",
|
| 169 |
+
"lower_is_better": false,
|
| 170 |
+
"score_type": "continuous",
|
| 171 |
+
"min_score": 0.0,
|
| 172 |
+
"max_score": 1.0,
|
| 173 |
+
"additional_details": {
|
| 174 |
+
"aggregation": "accuracy_over_subset",
|
| 175 |
+
"prompt_style": "5-shot CoT"
|
| 176 |
+
}
|
| 177 |
+
},
|
| 178 |
+
"score_details": {
|
| 179 |
+
"score": 0.4991
|
| 180 |
+
}
|
| 181 |
+
},
|
| 182 |
+
{
|
| 183 |
+
"evaluation_result_id": "mmlu_pro/computer_science",
|
| 184 |
+
"evaluation_name": "MMLU-Pro (Computer Science)",
|
| 185 |
+
"source_data": {
|
| 186 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 187 |
+
"source_type": "hf_dataset",
|
| 188 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 189 |
+
"hf_split": "train",
|
| 190 |
+
"additional_details": {
|
| 191 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 192 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 193 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 194 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 195 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 196 |
+
"dataset_total_questions": "12000",
|
| 197 |
+
"prompt_style": "5-shot CoT"
|
| 198 |
+
}
|
| 199 |
+
},
|
| 200 |
+
"metric_config": {
|
| 201 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
|
| 202 |
+
"metric_id": "mmlu_pro/computer_science",
|
| 203 |
+
"metric_name": "MMLU-Pro (Computer Science)",
|
| 204 |
+
"metric_kind": "accuracy",
|
| 205 |
+
"metric_unit": "proportion",
|
| 206 |
+
"lower_is_better": false,
|
| 207 |
+
"score_type": "continuous",
|
| 208 |
+
"min_score": 0.0,
|
| 209 |
+
"max_score": 1.0,
|
| 210 |
+
"additional_details": {
|
| 211 |
+
"aggregation": "accuracy_over_subset",
|
| 212 |
+
"prompt_style": "5-shot CoT"
|
| 213 |
+
}
|
| 214 |
+
},
|
| 215 |
+
"score_details": {
|
| 216 |
+
"score": 0.5415
|
| 217 |
+
}
|
| 218 |
+
},
|
| 219 |
+
{
|
| 220 |
+
"evaluation_result_id": "mmlu_pro/economics",
|
| 221 |
+
"evaluation_name": "MMLU-Pro (Economics)",
|
| 222 |
+
"source_data": {
|
| 223 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 224 |
+
"source_type": "hf_dataset",
|
| 225 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 226 |
+
"hf_split": "train",
|
| 227 |
+
"additional_details": {
|
| 228 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 229 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 230 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 231 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 232 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 233 |
+
"dataset_total_questions": "12000",
|
| 234 |
+
"prompt_style": "5-shot CoT"
|
| 235 |
+
}
|
| 236 |
+
},
|
| 237 |
+
"metric_config": {
|
| 238 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
|
| 239 |
+
"metric_id": "mmlu_pro/economics",
|
| 240 |
+
"metric_name": "MMLU-Pro (Economics)",
|
| 241 |
+
"metric_kind": "accuracy",
|
| 242 |
+
"metric_unit": "proportion",
|
| 243 |
+
"lower_is_better": false,
|
| 244 |
+
"score_type": "continuous",
|
| 245 |
+
"min_score": 0.0,
|
| 246 |
+
"max_score": 1.0,
|
| 247 |
+
"additional_details": {
|
| 248 |
+
"aggregation": "accuracy_over_subset",
|
| 249 |
+
"prompt_style": "5-shot CoT"
|
| 250 |
+
}
|
| 251 |
+
},
|
| 252 |
+
"score_details": {
|
| 253 |
+
"score": 0.7038
|
| 254 |
+
}
|
| 255 |
+
},
|
| 256 |
+
{
|
| 257 |
+
"evaluation_result_id": "mmlu_pro/engineering",
|
| 258 |
+
"evaluation_name": "MMLU-Pro (Engineering)",
|
| 259 |
+
"source_data": {
|
| 260 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 261 |
+
"source_type": "hf_dataset",
|
| 262 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 263 |
+
"hf_split": "train",
|
| 264 |
+
"additional_details": {
|
| 265 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 266 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 267 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 268 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 269 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 270 |
+
"dataset_total_questions": "12000",
|
| 271 |
+
"prompt_style": "5-shot CoT"
|
| 272 |
+
}
|
| 273 |
+
},
|
| 274 |
+
"metric_config": {
|
| 275 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
|
| 276 |
+
"metric_id": "mmlu_pro/engineering",
|
| 277 |
+
"metric_name": "MMLU-Pro (Engineering)",
|
| 278 |
+
"metric_kind": "accuracy",
|
| 279 |
+
"metric_unit": "proportion",
|
| 280 |
+
"lower_is_better": false,
|
| 281 |
+
"score_type": "continuous",
|
| 282 |
+
"min_score": 0.0,
|
| 283 |
+
"max_score": 1.0,
|
| 284 |
+
"additional_details": {
|
| 285 |
+
"aggregation": "accuracy_over_subset",
|
| 286 |
+
"prompt_style": "5-shot CoT"
|
| 287 |
+
}
|
| 288 |
+
},
|
| 289 |
+
"score_details": {
|
| 290 |
+
"score": 0.3787
|
| 291 |
+
}
|
| 292 |
+
},
|
| 293 |
+
{
|
| 294 |
+
"evaluation_result_id": "mmlu_pro/health",
|
| 295 |
+
"evaluation_name": "MMLU-Pro (Health)",
|
| 296 |
+
"source_data": {
|
| 297 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 298 |
+
"source_type": "hf_dataset",
|
| 299 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 300 |
+
"hf_split": "train",
|
| 301 |
+
"additional_details": {
|
| 302 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 303 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 304 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 305 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 306 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 307 |
+
"dataset_total_questions": "12000",
|
| 308 |
+
"prompt_style": "5-shot CoT"
|
| 309 |
+
}
|
| 310 |
+
},
|
| 311 |
+
"metric_config": {
|
| 312 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
|
| 313 |
+
"metric_id": "mmlu_pro/health",
|
| 314 |
+
"metric_name": "MMLU-Pro (Health)",
|
| 315 |
+
"metric_kind": "accuracy",
|
| 316 |
+
"metric_unit": "proportion",
|
| 317 |
+
"lower_is_better": false,
|
| 318 |
+
"score_type": "continuous",
|
| 319 |
+
"min_score": 0.0,
|
| 320 |
+
"max_score": 1.0,
|
| 321 |
+
"additional_details": {
|
| 322 |
+
"aggregation": "accuracy_over_subset",
|
| 323 |
+
"prompt_style": "5-shot CoT"
|
| 324 |
+
}
|
| 325 |
+
},
|
| 326 |
+
"score_details": {
|
| 327 |
+
"score": 0.6357
|
| 328 |
+
}
|
| 329 |
+
},
|
| 330 |
+
{
|
| 331 |
+
"evaluation_result_id": "mmlu_pro/history",
|
| 332 |
+
"evaluation_name": "MMLU-Pro (History)",
|
| 333 |
+
"source_data": {
|
| 334 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 335 |
+
"source_type": "hf_dataset",
|
| 336 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 337 |
+
"hf_split": "train",
|
| 338 |
+
"additional_details": {
|
| 339 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 340 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 341 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 342 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 343 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 344 |
+
"dataset_total_questions": "12000",
|
| 345 |
+
"prompt_style": "5-shot CoT"
|
| 346 |
+
}
|
| 347 |
+
},
|
| 348 |
+
"metric_config": {
|
| 349 |
+
"evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
|
| 350 |
+
"metric_id": "mmlu_pro/history",
|
| 351 |
+
"metric_name": "MMLU-Pro (History)",
|
| 352 |
+
"metric_kind": "accuracy",
|
| 353 |
+
"metric_unit": "proportion",
|
| 354 |
+
"lower_is_better": false,
|
| 355 |
+
"score_type": "continuous",
|
| 356 |
+
"min_score": 0.0,
|
| 357 |
+
"max_score": 1.0,
|
| 358 |
+
"additional_details": {
|
| 359 |
+
"aggregation": "accuracy_over_subset",
|
| 360 |
+
"prompt_style": "5-shot CoT"
|
| 361 |
+
}
|
| 362 |
+
},
|
| 363 |
+
"score_details": {
|
| 364 |
+
"score": 0.5722
|
| 365 |
+
}
|
| 366 |
+
},
|
| 367 |
+
{
|
| 368 |
+
"evaluation_result_id": "mmlu_pro/law",
|
| 369 |
+
"evaluation_name": "MMLU-Pro (Law)",
|
| 370 |
+
"source_data": {
|
| 371 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 372 |
+
"source_type": "hf_dataset",
|
| 373 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 374 |
+
"hf_split": "train",
|
| 375 |
+
"additional_details": {
|
| 376 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 377 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 378 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 379 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 380 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 381 |
+
"dataset_total_questions": "12000",
|
| 382 |
+
"prompt_style": "5-shot CoT"
|
| 383 |
+
}
|
| 384 |
+
},
|
| 385 |
+
"metric_config": {
|
| 386 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
|
| 387 |
+
"metric_id": "mmlu_pro/law",
|
| 388 |
+
"metric_name": "MMLU-Pro (Law)",
|
| 389 |
+
"metric_kind": "accuracy",
|
| 390 |
+
"metric_unit": "proportion",
|
| 391 |
+
"lower_is_better": false,
|
| 392 |
+
"score_type": "continuous",
|
| 393 |
+
"min_score": 0.0,
|
| 394 |
+
"max_score": 1.0,
|
| 395 |
+
"additional_details": {
|
| 396 |
+
"aggregation": "accuracy_over_subset",
|
| 397 |
+
"prompt_style": "5-shot CoT"
|
| 398 |
+
}
|
| 399 |
+
},
|
| 400 |
+
"score_details": {
|
| 401 |
+
"score": 0.3833
|
| 402 |
+
}
|
| 403 |
+
},
|
| 404 |
+
{
|
| 405 |
+
"evaluation_result_id": "mmlu_pro/math",
|
| 406 |
+
"evaluation_name": "MMLU-Pro (Math)",
|
| 407 |
+
"source_data": {
|
| 408 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 409 |
+
"source_type": "hf_dataset",
|
| 410 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 411 |
+
"hf_split": "train",
|
| 412 |
+
"additional_details": {
|
| 413 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 414 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 415 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 416 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 417 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 418 |
+
"dataset_total_questions": "12000",
|
| 419 |
+
"prompt_style": "5-shot CoT"
|
| 420 |
+
}
|
| 421 |
+
},
|
| 422 |
+
"metric_config": {
|
| 423 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
|
| 424 |
+
"metric_id": "mmlu_pro/math",
|
| 425 |
+
"metric_name": "MMLU-Pro (Math)",
|
| 426 |
+
"metric_kind": "accuracy",
|
| 427 |
+
"metric_unit": "proportion",
|
| 428 |
+
"lower_is_better": false,
|
| 429 |
+
"score_type": "continuous",
|
| 430 |
+
"min_score": 0.0,
|
| 431 |
+
"max_score": 1.0,
|
| 432 |
+
"additional_details": {
|
| 433 |
+
"aggregation": "accuracy_over_subset",
|
| 434 |
+
"prompt_style": "5-shot CoT"
|
| 435 |
+
}
|
| 436 |
+
},
|
| 437 |
+
"score_details": {
|
| 438 |
+
"score": 0.5218
|
| 439 |
+
}
|
| 440 |
+
},
|
| 441 |
+
{
|
| 442 |
+
"evaluation_result_id": "mmlu_pro/philosophy",
|
| 443 |
+
"evaluation_name": "MMLU-Pro (Philosophy)",
|
| 444 |
+
"source_data": {
|
| 445 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 446 |
+
"source_type": "hf_dataset",
|
| 447 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 448 |
+
"hf_split": "train",
|
| 449 |
+
"additional_details": {
|
| 450 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 451 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 452 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 453 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 454 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 455 |
+
"dataset_total_questions": "12000",
|
| 456 |
+
"prompt_style": "5-shot CoT"
|
| 457 |
+
}
|
| 458 |
+
},
|
| 459 |
+
"metric_config": {
|
| 460 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
|
| 461 |
+
"metric_id": "mmlu_pro/philosophy",
|
| 462 |
+
"metric_name": "MMLU-Pro (Philosophy)",
|
| 463 |
+
"metric_kind": "accuracy",
|
| 464 |
+
"metric_unit": "proportion",
|
| 465 |
+
"lower_is_better": false,
|
| 466 |
+
"score_type": "continuous",
|
| 467 |
+
"min_score": 0.0,
|
| 468 |
+
"max_score": 1.0,
|
| 469 |
+
"additional_details": {
|
| 470 |
+
"aggregation": "accuracy_over_subset",
|
| 471 |
+
"prompt_style": "5-shot CoT"
|
| 472 |
+
}
|
| 473 |
+
},
|
| 474 |
+
"score_details": {
|
| 475 |
+
"score": 0.5511
|
| 476 |
+
}
|
| 477 |
+
},
|
| 478 |
+
{
|
| 479 |
+
"evaluation_result_id": "mmlu_pro/physics",
|
| 480 |
+
"evaluation_name": "MMLU-Pro (Physics)",
|
| 481 |
+
"source_data": {
|
| 482 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 483 |
+
"source_type": "hf_dataset",
|
| 484 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 485 |
+
"hf_split": "train",
|
| 486 |
+
"additional_details": {
|
| 487 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 488 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 489 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 490 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 491 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 492 |
+
"dataset_total_questions": "12000",
|
| 493 |
+
"prompt_style": "5-shot CoT"
|
| 494 |
+
}
|
| 495 |
+
},
|
| 496 |
+
"metric_config": {
|
| 497 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
|
| 498 |
+
"metric_id": "mmlu_pro/physics",
|
| 499 |
+
"metric_name": "MMLU-Pro (Physics)",
|
| 500 |
+
"metric_kind": "accuracy",
|
| 501 |
+
"metric_unit": "proportion",
|
| 502 |
+
"lower_is_better": false,
|
| 503 |
+
"score_type": "continuous",
|
| 504 |
+
"min_score": 0.0,
|
| 505 |
+
"max_score": 1.0,
|
| 506 |
+
"additional_details": {
|
| 507 |
+
"aggregation": "accuracy_over_subset",
|
| 508 |
+
"prompt_style": "5-shot CoT"
|
| 509 |
+
}
|
| 510 |
+
},
|
| 511 |
+
"score_details": {
|
| 512 |
+
"score": 0.4935
|
| 513 |
+
}
|
| 514 |
+
},
|
| 515 |
+
{
|
| 516 |
+
"evaluation_result_id": "mmlu_pro/psychology",
|
| 517 |
+
"evaluation_name": "MMLU-Pro (Psychology)",
|
| 518 |
+
"source_data": {
|
| 519 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 520 |
+
"source_type": "hf_dataset",
|
| 521 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 522 |
+
"hf_split": "train",
|
| 523 |
+
"additional_details": {
|
| 524 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 525 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 526 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 527 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 528 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 529 |
+
"dataset_total_questions": "12000",
|
| 530 |
+
"prompt_style": "5-shot CoT"
|
| 531 |
+
}
|
| 532 |
+
},
|
| 533 |
+
"metric_config": {
|
| 534 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
|
| 535 |
+
"metric_id": "mmlu_pro/psychology",
|
| 536 |
+
"metric_name": "MMLU-Pro (Psychology)",
|
| 537 |
+
"metric_kind": "accuracy",
|
| 538 |
+
"metric_unit": "proportion",
|
| 539 |
+
"lower_is_better": false,
|
| 540 |
+
"score_type": "continuous",
|
| 541 |
+
"min_score": 0.0,
|
| 542 |
+
"max_score": 1.0,
|
| 543 |
+
"additional_details": {
|
| 544 |
+
"aggregation": "accuracy_over_subset",
|
| 545 |
+
"prompt_style": "5-shot CoT"
|
| 546 |
+
}
|
| 547 |
+
},
|
| 548 |
+
"score_details": {
|
| 549 |
+
"score": 0.7343
|
| 550 |
+
}
|
| 551 |
+
},
|
| 552 |
+
{
|
| 553 |
+
"evaluation_result_id": "mmlu_pro/other",
|
| 554 |
+
"evaluation_name": "MMLU-Pro (Other)",
|
| 555 |
+
"source_data": {
|
| 556 |
+
"dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
|
| 557 |
+
"source_type": "hf_dataset",
|
| 558 |
+
"hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
|
| 559 |
+
"hf_split": "train",
|
| 560 |
+
"additional_details": {
|
| 561 |
+
"results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
|
| 562 |
+
"leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
|
| 563 |
+
"benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
|
| 564 |
+
"paper_url": "https://arxiv.org/abs/2406.01574",
|
| 565 |
+
"github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
|
| 566 |
+
"dataset_total_questions": "12000",
|
| 567 |
+
"prompt_style": "5-shot CoT"
|
| 568 |
+
}
|
| 569 |
+
},
|
| 570 |
+
"metric_config": {
|
| 571 |
+
"evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
|
| 572 |
+
"metric_id": "mmlu_pro/other",
|
| 573 |
+
"metric_name": "MMLU-Pro (Other)",
|
| 574 |
+
"metric_kind": "accuracy",
|
| 575 |
+
"metric_unit": "proportion",
|
| 576 |
+
"lower_is_better": false,
|
| 577 |
+
"score_type": "continuous",
|
| 578 |
+
"min_score": 0.0,
|
| 579 |
+
"max_score": 1.0,
|
| 580 |
+
"additional_details": {
|
| 581 |
+
"aggregation": "accuracy_over_subset",
|
| 582 |
+
"prompt_style": "5-shot CoT"
|
| 583 |
+
}
|
| 584 |
+
},
|
| 585 |
+
"score_details": {
|
| 586 |
+
"score": 0.6028
|
| 587 |
+
}
|
| 588 |
+
}
|
| 589 |
+
]
|
| 590 |
+
}
|