| --- |
| language: |
| - en |
| license: apache-2.0 |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - dense |
| - generated_from_trainer |
| - dataset_size:5600 |
| - loss:MatryoshkaLoss |
| - loss:MultipleNegativesRankingLoss |
| base_model: Qwen/Qwen3-Embedding-0.6B |
| widget: |
| - source_sentence: What were the total assets at fair value on December 31, 2023? |
| sentences: |
| - In addition to its contractual cash requirements, the Company has an authorized |
| share repurchase program. The program does not obligate the Company to acquire |
| a minimum amount of shares. As of September 30, 2023, the Company’s quarterly |
| cash dividend was $0.24 per share. |
| - Effective January 1, 2023, we prospectively adopted new guidance that eliminated |
| the recognition and measurement of TDRs. We evaluate all loans and receivables |
| restructurings according to accounting guidance for loan refinancing and restructuring. |
| Modifications to loans and receivables primarily include temporary interest rate |
| reductions and placing the customer on a fixed payment plan not to exceed 60 months. |
| - Total assets at fair value on December 31, 2023 were reported to be $71,921 million. |
| - source_sentence: What were the key factors affecting the company's cash flow from |
| operations in fiscal 2023? |
| sentences: |
| - General and administrative | $ | 950 | | $ | 2,025 | 113 | % Percentage of revenue |
| | 11 | % | 20 | % | General and administrative expense increased $1.1 billion, |
| or 113%, in 2023, compared to 2022. |
| - Within two months after submission of each annual execution proposal, the Macao |
| government will decide on their approval, and may request adjustments to specific |
| projects, to the investment amount and to the execution schedule. |
| - The company's cash flow from operations in fiscal 2023 was affected by various |
| factors including changes in working capital components like accounts payable, |
| inventories, and accounts receivable. |
| - source_sentence: What percentage of the total U.S. dialysis patient service revenues |
| were generated from government-based programs in 2023? |
| sentences: |
| - The document includes a 'Glossary of Terms and Acronyms' that provides definitions |
| and explanations of financial terms used. |
| - Profit before taxes for 2022 was $8,752 million and rose to $13,050 million in |
| 2023. |
| - In 2023, approximately 67% of the total U.S. dialysis patient service revenues |
| were generated from government-based programs. |
| - source_sentence: What was the effective income tax rate for the Company in 2023? |
| sentences: |
| - Chevron's oil-equivalent production in the UK has increased by approximately 4 |
| percent from 2022 to 2023, as indicated in the production summary tables. |
| - The Company’s effective income tax rate decreased to 25.1% in 2023 compared to |
| 25.9% in the prior year. |
| - In 2023, 45% of our consolidated Gross Merchandise Sales was generated when a |
| seller or buyer, or both, were located outside of the United States. |
| - source_sentence: Which section of the financial document addresses Financial Statements |
| and Supplementary Data? |
| sentences: |
| - The gift card liability was $145,014 in 2022 and increased to $164,930 in 2023. |
| - The 7% Notes due 2029 are scheduled to mature on February 15, 2029. |
| - Financial Statements and Supplementary Data are addressed in Item 8 of the financial |
| document. |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| metrics: |
| - cosine_accuracy@1 |
| - cosine_accuracy@3 |
| - cosine_accuracy@5 |
| - cosine_accuracy@10 |
| - cosine_precision@1 |
| - cosine_precision@3 |
| - cosine_precision@5 |
| - cosine_precision@10 |
| - cosine_recall@1 |
| - cosine_recall@3 |
| - cosine_recall@5 |
| - cosine_recall@10 |
| - cosine_ndcg@10 |
| - cosine_mrr@10 |
| - cosine_map@100 |
| model-index: |
| - name: 'Qwen3 base Financial ' |
| results: |
| - task: |
| type: information-retrieval |
| name: Information Retrieval |
| dataset: |
| name: dim 1024 |
| type: dim_1024 |
| metrics: |
| - type: cosine_accuracy@1 |
| value: 0.7507142857142857 |
| name: Cosine Accuracy@1 |
| - type: cosine_accuracy@3 |
| value: 0.8707142857142857 |
| name: Cosine Accuracy@3 |
| - type: cosine_accuracy@5 |
| value: 0.8985714285714286 |
| name: Cosine Accuracy@5 |
| - type: cosine_accuracy@10 |
| value: 0.9364285714285714 |
| name: Cosine Accuracy@10 |
| - type: cosine_precision@1 |
| value: 0.7507142857142857 |
| name: Cosine Precision@1 |
| - type: cosine_precision@3 |
| value: 0.29023809523809524 |
| name: Cosine Precision@3 |
| - type: cosine_precision@5 |
| value: 0.1797142857142857 |
| name: Cosine Precision@5 |
| - type: cosine_precision@10 |
| value: 0.09364285714285712 |
| name: Cosine Precision@10 |
| - type: cosine_recall@1 |
| value: 0.7507142857142857 |
| name: Cosine Recall@1 |
| - type: cosine_recall@3 |
| value: 0.8707142857142857 |
| name: Cosine Recall@3 |
| - type: cosine_recall@5 |
| value: 0.8985714285714286 |
| name: Cosine Recall@5 |
| - type: cosine_recall@10 |
| value: 0.9364285714285714 |
| name: Cosine Recall@10 |
| - type: cosine_ndcg@10 |
| value: 0.846090041345316 |
| name: Cosine Ndcg@10 |
| - type: cosine_mrr@10 |
| value: 0.8169348072562356 |
| name: Cosine Mrr@10 |
| - type: cosine_map@100 |
| value: 0.8197317550291238 |
| name: Cosine Map@100 |
| --- |
| |
| # Qwen3 base Financial |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) on the json dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) <!-- at revision c54f2e6e80b2d7b7de06f51cec4959f6b3e03418 --> |
| - **Maximum Sequence Length:** 32768 tokens |
| - **Output Dimensionality:** 1024 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Training Dataset:** |
| - json |
| - **Language:** en |
| - **License:** apache-2.0 |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'}) |
| (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True}) |
| (2): Normalize() |
| ) |
| ``` |
|
|
| ## Usage |
|
|
| ### Direct Usage (Sentence Transformers) |
|
|
| First install the Sentence Transformers library: |
|
|
| ```bash |
| pip install -U sentence-transformers |
| ``` |
|
|
| Then you can load this model and run inference. |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("PhilipCisco/qwen3-base-financial2") |
| # Run inference |
| queries = [ |
| "Which section of the financial document addresses Financial Statements and Supplementary Data?", |
| ] |
| documents = [ |
| 'Financial Statements and Supplementary Data are addressed in Item 8 of the financial document.', |
| 'The 7% Notes due 2029 are scheduled to mature on February 15, 2029.', |
| 'The gift card liability was $145,014 in 2022 and increased to $164,930 in 2023.', |
| ] |
| query_embeddings = model.encode_query(queries) |
| document_embeddings = model.encode_document(documents) |
| print(query_embeddings.shape, document_embeddings.shape) |
| # [1, 1024] [3, 1024] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(query_embeddings, document_embeddings) |
| print(similarities) |
| # tensor([[0.7375, 0.1121, 0.0035]]) |
| ``` |
|
|
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## Evaluation |
|
|
| ### Metrics |
|
|
| #### Information Retrieval |
|
|
| * Dataset: `dim_1024` |
| * Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: |
| ```json |
| { |
| "truncate_dim": 1024 |
| } |
| ``` |
|
|
| | Metric | Value | |
| |:--------------------|:-----------| |
| | cosine_accuracy@1 | 0.7507 | |
| | cosine_accuracy@3 | 0.8707 | |
| | cosine_accuracy@5 | 0.8986 | |
| | cosine_accuracy@10 | 0.9364 | |
| | cosine_precision@1 | 0.7507 | |
| | cosine_precision@3 | 0.2902 | |
| | cosine_precision@5 | 0.1797 | |
| | cosine_precision@10 | 0.0936 | |
| | cosine_recall@1 | 0.7507 | |
| | cosine_recall@3 | 0.8707 | |
| | cosine_recall@5 | 0.8986 | |
| | cosine_recall@10 | 0.9364 | |
| | **cosine_ndcg@10** | **0.8461** | |
| | cosine_mrr@10 | 0.8169 | |
| | cosine_map@100 | 0.8197 | |
| |
| <!-- |
| ## Bias, Risks and Limitations |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## Training Details |
| |
| ### Training Dataset |
| |
| #### json |
| |
| * Dataset: json |
| * Size: 5,600 training samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | |
| |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 7 tokens</li><li>mean: 20.73 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 47.95 tokens</li><li>max: 431 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:--------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
| | <code>What was the increase in sales and marketing expenses for the year ended December 31, 2023 compared to 2022?</code> | <code>Sales and marketing expenses increased by $42.5 million, or 6%, for the year ended December 31, 2023 compared to 2022.</code> | |
| | <code>What method is used to provide information about legal proceedings in the Annual Report on Form 10-K?</code> | <code>Information about legal proceedings in the Annual Report on Form 10-K is incorporated by reference under several notes and sections.</code> | |
| | <code>How did selling, distribution, and administration expenses change in 2023 compared to previous years?</code> | <code>In 2023, the decline in Selling, distribution and administration expense was driven by lower compensation expense associated with workforce reductions, lower costs for professional services and lower freight and warehousing expenses as a result of lower shipments during 2023. Additionally, Selling, distribution and administration expense in 2023 included $116.0 million of intangible asset impairment charges as compared to $281.0 million of intangible asset impairment charges in 2022.</code> | |
| * Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
| ```json |
| { |
| "loss": "MultipleNegativesRankingLoss", |
| "matryoshka_dims": [ |
| 1024 |
| ], |
| "matryoshka_weights": [ |
| 1 |
| ], |
| "n_dims_per_step": -1 |
| } |
| ``` |
| |
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
| |
| - `eval_strategy`: epoch |
| - `per_device_train_batch_size`: 4 |
| - `per_device_eval_batch_size`: 4 |
| - `gradient_accumulation_steps`: 8 |
| - `learning_rate`: 2e-05 |
| - `num_train_epochs`: 4 |
| - `lr_scheduler_type`: cosine |
| - `warmup_ratio`: 0.1 |
| - `bf16`: True |
| - `tf32`: True |
| - `load_best_model_at_end`: True |
| - `batch_sampler`: no_duplicates |
| |
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
| |
| - `overwrite_output_dir`: False |
| - `do_predict`: False |
| - `eval_strategy`: epoch |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 4 |
| - `per_device_eval_batch_size`: 4 |
| - `per_gpu_train_batch_size`: None |
| - `per_gpu_eval_batch_size`: None |
| - `gradient_accumulation_steps`: 8 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 2e-05 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 4 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: cosine |
| - `lr_scheduler_kwargs`: {} |
| - `warmup_ratio`: 0.1 |
| - `warmup_steps`: 0 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `save_safetensors`: True |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `no_cuda`: False |
| - `use_cpu`: False |
| - `use_mps_device`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `jit_mode_eval`: False |
| - `use_ipex`: False |
| - `bf16`: True |
| - `fp16`: False |
| - `fp16_opt_level`: O1 |
| - `half_precision_backend`: auto |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: True |
| - `local_rank`: 0 |
| - `ddp_backend`: None |
| - `tpu_num_cores`: None |
| - `tpu_metrics_debug`: False |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `past_index`: -1 |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: True |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_min_num_params`: 0 |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
| - `fsdp_transformer_layer_cls_to_wrap`: None |
| - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
| - `parallelism_config`: None |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `adafactor`: False |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `skip_memory_metrics`: True |
| - `use_legacy_prediction_loop`: False |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_inputs_for_metrics`: False |
| - `include_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `fp16_backend`: auto |
| - `push_to_hub_model_id`: None |
| - `push_to_hub_organization`: None |
| - `mp_parameters`: |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `torchdynamo`: None |
| - `ray_scope`: last |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `include_tokens_per_second`: False |
| - `include_num_input_tokens_seen`: False |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: False |
| - `prompts`: None |
| - `batch_sampler`: no_duplicates |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
| |
| </details> |
| |
| ### Training Logs |
| | Epoch | Step | Training Loss | dim_1024_cosine_ndcg@10 | |
| |:-------:|:-------:|:-------------:|:-----------------------:| |
| | -1 | -1 | - | 0.7762 | |
| | 0.1713 | 10 | 0.0243 | - | |
| | 0.3426 | 20 | 0.0269 | - | |
| | 0.5139 | 30 | 0.0171 | - | |
| | 0.6852 | 40 | 0.0224 | - | |
| | 0.8565 | 50 | 0.0376 | - | |
| | 1.0 | 59 | - | 0.8200 | |
| | 1.0171 | 60 | 0.0221 | - | |
| | 1.1884 | 70 | 0.0089 | - | |
| | 1.3597 | 80 | 0.0127 | - | |
| | 1.5310 | 90 | 0.0116 | - | |
| | 1.7024 | 100 | 0.0086 | - | |
| | 1.8737 | 110 | 0.0113 | - | |
| | 2.0 | 118 | - | 0.8280 | |
| | 2.0343 | 120 | 0.0074 | - | |
| | 2.2056 | 130 | 0.0077 | - | |
| | 2.3769 | 140 | 0.0107 | - | |
| | 2.5482 | 150 | 0.0089 | - | |
| | 2.7195 | 160 | 0.0098 | - | |
| | 2.8908 | 170 | 0.006 | - | |
| | **3.0** | **177** | **-** | **0.8448** | |
| | 3.0514 | 180 | 0.0111 | - | |
| | 3.2227 | 190 | 0.0074 | - | |
| | 3.3940 | 200 | 0.0082 | - | |
| | 3.5653 | 210 | 0.0047 | - | |
| | 3.7366 | 220 | 0.0076 | - | |
| | 3.9079 | 230 | 0.0085 | - | |
| | 4.0 | 236 | - | 0.8461 | |
|
|
| * The bold row denotes the saved checkpoint. |
|
|
| ### Framework Versions |
| - Python: 3.10.14 |
| - Sentence Transformers: 5.1.0 |
| - Transformers: 4.56.1 |
| - PyTorch: 2.8.0+cu128 |
| - Accelerate: 1.10.1 |
| - Datasets: 2.19.1 |
| - Tokenizers: 0.22.0 |
|
|
| ## Citation |
|
|
| ### BibTeX |
|
|
| #### Sentence Transformers |
| ```bibtex |
| @inproceedings{reimers-2019-sentence-bert, |
| title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
| author = "Reimers, Nils and Gurevych, Iryna", |
| booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
| month = "11", |
| year = "2019", |
| publisher = "Association for Computational Linguistics", |
| url = "https://arxiv.org/abs/1908.10084", |
| } |
| ``` |
|
|
| #### MatryoshkaLoss |
| ```bibtex |
| @misc{kusupati2024matryoshka, |
| title={Matryoshka Representation Learning}, |
| author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
| year={2024}, |
| eprint={2205.13147}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG} |
| } |
| ``` |
|
|
| #### MultipleNegativesRankingLoss |
| ```bibtex |
| @misc{henderson2017efficient, |
| title={Efficient Natural Language Response Suggestion for Smart Reply}, |
| author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
| year={2017}, |
| eprint={1705.00652}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
|
|
| <!-- |
| ## Glossary |
|
|
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
|
|
| <!-- |
| ## Model Card Authors |
|
|
| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
| --> |
|
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| <!-- |
| ## Model Card Contact |
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|
| *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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