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--- |
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library_name: transformers |
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tags: |
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- ner |
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- biomedical |
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- disease-recognition |
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- pubmedbert |
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- BioMedNLP |
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datasets: |
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- rjac/biobert-ner-diseases-dataset |
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license: mit |
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language: |
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- en |
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metrics: |
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- precision |
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- recall |
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- f1 |
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base_model: |
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- microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext |
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--- |
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# Model Card for BioMed-NER-English |
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Fine-tuned BiomedNLP-BiomedBERT model for medical entity recognition, achieving 0.9868 F1-score on disease entity extraction from clinical text. |
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## Model Details |
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### Model Description |
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- **Developed by:** [Aashish Acharya](https://github.com/acharya-jyu) |
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- **Model type:** BiomedNLP-BiomedBERT (Token Classification) |
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- **Language(s):** English |
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- **License:** MIT |
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- **Finetuned from model:** microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext |
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- **Source Code:** [GitHub Link](https://github.com/Acharya-jyu/ner-model) |
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### Model Sources |
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- **Base Model:** [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) |
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- **Training Dataset:** [rjac/biobert-ner-diseases-dataset](https://huggingface.co/datasets/rjac/biobert-ner-diseases-dataset) |
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## Uses |
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### Direct Use |
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This model excels at extracting disease mentions from medical text using BIO tagging scheme: |
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- B-Disease: Beginning of disease mention |
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- I-Disease: Continuation of disease mention |
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- O: Non-disease tokens |
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### Training |
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**Training Data** |
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Dataset: biobert-ner-diseases-dataset |
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Size: 21,225 annotated medical sentences |
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Split: 15,488 training (73%), 5,737 testing (27%) |
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Average sentence length: 24.3 tokens |
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Disease mention frequency: 1.8 per sentence |
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**Training Procedure** |
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**Training Hyperparameters** |
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- Learning rate: 5e-5 |
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- Batch size: 8 |
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- Epochs: 8 |
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- Optimizer: AdamW with weight decay (0.01) |
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- Warmup steps: 500 |
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- Early stopping patience: 5 |
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- Loss function: Cross-entropy with label smoothing (0.1) |
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- Gradient accumulation steps: 4 |
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- Max gradient norm: 1.0 |
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**Evaluation** |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/662757230601587f0be9781b/cOW2y9C8ypND8f7lpFC0W.png" width="400" alt="image"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/662757230601587f0be9781b/vn5UZUFhkuaz78QvnP01O.png" width="400" alt="image"> |
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**Metrics** |
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Final model performancc |
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**Strict Entity Matching:** |
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Precision: 0.9869 |
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Recall: 0.9868 |
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F1 Score: 0.9868 |
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**Partial Entity Matching:** |
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Precision: 0.9527 |
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Recall: 0.9456 |
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F1 Score: 0.9491 |
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**Error Analysis** |
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Boundary Errors: 1,154 |
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Type Errors: 0 |
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**Environmental Impact** |
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Hardware Type: Google Colab GPU |
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Hours used: ~2 hours |
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Cloud Provider: Google Cloud |
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Carbon Emitted: Not tracked |
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**Technical Specifications** |
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Model Architecture |
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Base model: PubMedBERT |
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Hidden size: 768 |
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Attention heads: 12 |
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Layers: 12 |
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Parameters: ~110M |
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**Compute Infrastructure** |
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Platform: Google Colab |
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GPU: Tesla T4/P100 |
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## Citation |
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```bibtex |
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@misc{acharya2024sapbert, |
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title={SapBERT-PubMedBERT Fine-tuned on DDXPlus Dataset}, |
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author={Acharya, Aashish}, |
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year={2024}, |
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publisher={Hugging Face Model Hub} |
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} |
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``` |
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## Model Card Contact |
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[Aashish Acharya](https://github.com/acharya-jyu) |
