Upload OGBert-110M sentence model

1_Pooling/config.json

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+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- modernbert
+- embeddings
+pipeline_tag: sentence-similarity
+datasets:
+- mjbommar/ogbert-v1-mlm
+model-index:
+- name: ogbert-110m-sentence
+  results:
+  - task:
+      type: clustering
+    dataset:
+      name: Custom Domain Clustering
+      type: custom
+    metrics:
+    - type: adjusted_rand_index
+      value: 0.941
+  - task:
+      type: retrieval
+    dataset:
+      name: Custom Domain Retrieval
+      type: custom
+    metrics:
+    - type: mrr
+      value: 0.959
+---
+
+# OGBert-110M-Sentence
+
+A 110M parameter ModernBERT-based sentence embedding model for glossary and domain-specific text.
+
+**Related models:**
+- [mjbommar/ogbert-110m-base](https://huggingface.co/mjbommar/ogbert-110m-base) - Base MLM model for fill-mask tasks
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Architecture | ModernBERT + Mean Pooling + L2 Normalize |
+| Parameters | 110M |
+| Hidden size | 768 |
+| Layers | 12 |
+| Attention heads | 12 |
+| Vocab size | 32,768 |
+| Max sequence | 1,024 tokens |
+| Embedding dim | 768 (L2 normalized) |
+
+## Training
+
+- **Pretraining**: Masked Language Modeling on domain-specific glossary corpus
+- **Dataset**: [mjbommar/ogbert-v1-mlm](https://huggingface.co/datasets/mjbommar/ogbert-v1-mlm) - derived from [OpenGloss](https://arxiv.org/abs/2511.18622), a synthetic encyclopedic dictionary with 537K senses across 150K lexemes
+- **Checkpoint**: Step 8K (selected for optimal downstream performance)
+- **Key finding**: L2 normalization of embeddings is critical for clustering/retrieval performance
+
+## Performance
+
+### Document Clustering (ARI)
+
+Evaluated on 80 domain-specific documents across 10 categories using KMeans clustering.
+
+| Model | Params | ARI | Cluster Acc |
+|-------|--------|-----|-------------|
+| **OGBert-110M-Sentence** | **110M** | **0.941** | **0.975** |
+| BERT-base | 110M | 0.896 | 0.950 |
+| RoBERTa-base | 125M | 0.941 | 0.975 |
+| MiniLM-L6-v2 | 22M | 0.833 | 0.925 |
+
+OGBert-110M-Sentence matches RoBERTa-base on clustering and **beats MiniLM by 13%**.
+
+### Document Retrieval (MRR)
+
+Mean Reciprocal Rank for same-category document retrieval.
+
+| Model | Params | Sample MRR |
+|-------|--------|------------|
+| **OGBert-110M-Sentence** | **110M** | **0.959** |
+| BERT-base | 110M | 0.994 |
+| RoBERTa-base | 125M | 0.989 |
+| MiniLM-L6-v2 | 22M | 0.958 |
+
+### Word Similarity (SimLex-999)
+
+Spearman correlation between model cosine similarities and human judgments.
+
+| Model | Params | SimLex-999 (ρ) |
+|-------|--------|----------------|
+| **OGBert-110M-Sentence** | **110M** | **0.345** |
+| BERT-base | 110M | 0.070 |
+| RoBERTa-base | 125M | -0.061 |
+
+OGBert-110M-Sentence achieves **5x better** word similarity than BERT-base.
+
+### Summary vs Baselines
+
+At the same size as BERT-base, OGBert-110M-Sentence achieves:
+- **5x better** word similarity (SimLex)
+- **Matches** RoBERTa on clustering (ARI)
+- **13% better** clustering than MiniLM
+
+## Usage
+
+### Sentence-Transformers (Recommended)
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer('mjbommar/ogbert-110m-sentence')
+embeddings = model.encode(['your text here'])  # L2 normalized by default
+```
+
+**Example - Domain Similarity:**
+```python
+sentences = [
+    'The financial audit revealed discrepancies in the quarterly report.',
+    'An accounting review found errors in the fiscal statement.',
+    'The patient was diagnosed with acute respiratory infection.',
+]
+embeddings = model.encode(sentences)
+```
+
+The model correctly identifies higher similarity within the same domain.
+
+### Direct Transformers Usage
+
+```python
+from transformers import AutoModel, AutoTokenizer
+import torch.nn.functional as F
+
+tokenizer = AutoTokenizer.from_pretrained('mjbommar/ogbert-110m-sentence')
+model = AutoModel.from_pretrained('mjbommar/ogbert-110m-sentence')
+
+inputs = tokenizer('your text here', return_tensors='pt', padding=True, truncation=True)
+outputs = model(**inputs)
+
+# Mean pooling + L2 normalize (critical for performance)
+mask = inputs['attention_mask'].unsqueeze(-1)
+pooled = (outputs.last_hidden_state * mask).sum(1) / mask.sum(1)
+embeddings = F.normalize(pooled, p=2, dim=1)
+```
+
+### For Fill-Mask Tasks
+
+Use [mjbommar/ogbert-110m-base](https://huggingface.co/mjbommar/ogbert-110m-base) instead.
+
+## Citation
+
+If you use this model, please cite the OpenGloss dataset:
+
+```bibtex
+@article{bommarito2025opengloss,
+  title={OpenGloss: A Synthetic Encyclopedic Dictionary and Semantic Knowledge Graph},
+  author={Bommarito II, Michael J.},
+  journal={arXiv preprint arXiv:2511.18622},
+  year={2025}
+}
+```
+
+## License
+
+Apache 2.0

config.json

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+{
+  "_name_or_path": "mjbommar/ogbert-110m-sentence",
+  "architectures": [
+    "ModernBertModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "cls",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "hidden_act": "gelu",
+  "hidden_activation": "gelu",
+  "hidden_size": 768,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "layer_types": [
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention"
+  ],
+  "local_attention": 128,
+  "max_position_embeddings": 1024,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 2,
+  "repad_logits_with_grad": false,
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 160000.0,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 10000.0,
+      "rope_type": "default"
+    }
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.0",
+  "vocab_size": 32768
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1820508556341b6e8a9f5bb17f174cd1bbc3ff3847d33e4fee547bae42714d7c
+size 556226256

modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

special_tokens_map.json

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+{
+  "bos_token": "<|start|>",
+  "cls_token": "<|cls|>",
+  "eos_token": "<|end|>",
+  "mask_token": "<|mask|>",
+  "pad_token": "<|pad|>",
+  "sep_token": "<|sep|>",
+  "unk_token": "<|unk|>"
+}

tokenizer_config.json

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+{
+  "additional_special_tokens": null,
+  "backend": "tokenizers",
+  "bos_token": "<|start|>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<|cls|>",
+  "eos_token": "<|end|>",
+  "extra_special_tokens": [],
+  "is_local": false,
+  "mask_token": "<|mask|>",
+  "model_max_length": 1024,
+  "model_type": "modernbert",
+  "pad_token": "<|pad|>",
+  "sep_token": "<|sep|>",
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<|unk|>",
+  "vocab_size": 32767
+}