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README.md

@@ -1,199 +1,124 @@
 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
-tags: []
+tags:
+- transformers
+- modernbert
+- fill-mask
+- masked-language-model
+pipeline_tag: fill-mask
+datasets:
+- mjbommar/ogbert-v1-mlm
+model-index:
+- name: ogbert-110m-base
+  results:
+  - task:
+      type: word-similarity
+    dataset:
+      name: SimLex-999
+      type: simlex999
+    metrics:
+    - type: spearman
+      value: 0.345
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# OGBert-110M-Base
 
+A 110M parameter ModernBERT-based masked language model trained on glossary and domain-specific text.
 
+**Related models:**
+- [mjbommar/ogbert-110m-sentence](https://huggingface.co/mjbommar/ogbert-110m-sentence) - Sentence embedding version with mean pooling + L2 normalization
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+| Property | Value |
+|----------|-------|
+| Architecture | ModernBERT |
+| Parameters | 110M |
+| Hidden size | 768 |
+| Layers | 12 |
+| Attention heads | 12 |
+| Vocab size | 32,768 |
+| Max sequence | 1,024 tokens |
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+## Training
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+- **Task**: Masked Language Modeling (MLM)
+- **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
+- **Masking**: Standard 15% token masking
+- **Training steps**: 8,000 steps (selected for optimal downstream performance)
+- **Tokens processed**: ~4.5B
+- **Batch size**: 1,024
+- **Peak learning rate**: 3e-4
 
-## Technical Specifications [optional]
+## Performance
 
-### Model Architecture and Objective
+### Word Similarity (SimLex-999)
 
-[More Information Needed]
+**SimLex-999** measures Spearman correlation between model cosine similarities and human judgments on 999 word pairs. Higher = better alignment with human perception of word similarity.
 
-### Compute Infrastructure
+| Model | Params | SimLex-999 (ρ) |
+|-------|--------|----------------|
+| **OGBert-110M-Base** | **110M** | **0.345** |
+| BERT-base | 110M | 0.070 |
+| RoBERTa-base | 125M | -0.061 |
 
-[More Information Needed]
+OGBert-110M-Base achieves **5x better** word similarity than BERT-base with the same parameter count.
 
-#### Hardware
+### Document Clustering
 
-[More Information Needed]
+Evaluated on 80 domain-specific documents across 10 categories using KMeans.
 
-#### Software
+| Model | Params | ARI | Cluster Acc |
+|-------|--------|-----|-------------|
+| **OGBert-110M-Base** | **110M** | **0.941** | **0.975** |
+| BERT-base | 110M | 0.896 | 0.950 |
+| RoBERTa-base | 125M | 0.941 | 0.975 |
 
-[More Information Needed]
+OGBert-110M-Base matches or exceeds RoBERTa-base on clustering tasks.
 
-## Citation [optional]
+## Usage
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+### Fill-Mask Pipeline
 
-**BibTeX:**
+```python
+from transformers import pipeline
 
-[More Information Needed]
+fill_mask = pipeline('fill-mask', model='mjbommar/ogbert-110m-base')
+result = fill_mask('The financial <|mask|> was approved.')
+```
 
-**APA:**
+### Direct Model Usage
 
-[More Information Needed]
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
 
-## Glossary [optional]
+tokenizer = AutoTokenizer.from_pretrained('mjbommar/ogbert-110m-base')
+model = AutoModelForMaskedLM.from_pretrained('mjbommar/ogbert-110m-base')
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+inputs = tokenizer('The <|mask|> definition is clear.', return_tensors='pt')
+outputs = model(**inputs)
+```
 
-[More Information Needed]
+### For Sentence Embeddings
 
-## More Information [optional]
+Use [mjbommar/ogbert-110m-sentence](https://huggingface.co/mjbommar/ogbert-110m-sentence) instead, which includes mean pooling and L2 normalization for optimal similarity search.
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model, please cite the OpenGloss dataset:
 
-[More Information Needed]
+```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}
+}
+```
 
-## Model Card Contact
+## License
 
-[More Information Needed]
+Apache 2.0