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--- |
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library_name: transformers |
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tags: |
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- biomedical-nlp |
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- language-model |
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- bert |
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- medical-text |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- michiyasunaga/BioLinkBERT-large |
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pipeline_tag: text-classification |
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--- |
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# Model Card for BioLinkBERT |
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## Model Details |
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### Model Description |
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BioLinkBERT is a specialized language model designed for biomedical natural language processing tasks. It leverages advanced techniques to understand and process medical and scientific text with high accuracy and context-awareness. |
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- **Developed by:** [Research Institution/Team Name - to be specified] |
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- **Model type:** Transformer-based Biomedical Language Model |
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- **Language(s):** English (Biomedical Domain) |
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- **License:** [Specific License - to be added] |
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- **Finetuned from model:** Base BERT or BioBERT model |
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### Model Sources |
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- **Repository:** [GitHub/Model Repository Link] |
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- **Paper:** [Research Publication Link] |
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- **Demo:** [Optional Demo URL] |
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## Uses |
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### Direct Use |
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BioLinkBERT can be applied to various biomedical natural language processing tasks, including: |
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- Medical text classification |
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- Biomedical named entity recognition |
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- Scientific literature analysis |
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- Clinical document understanding |
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### Downstream Use |
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Potential applications include: |
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- Clinical decision support systems |
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- Biomedical research information extraction |
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- Medical literature summarization |
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- Semantic analysis of healthcare documents |
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### Out-of-Scope Use |
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- Not intended for direct medical diagnosis |
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- Performance may degrade outside biomedical domain |
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- Should not replace professional medical interpretation |
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## Bias, Risks, and Limitations |
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- Potential biases from training data |
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- Limited to biomedical text domains |
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- May not capture the most recent medical terminologies |
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- Requires careful validation in critical applications |
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### Recommendations |
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- Use as a supporting tool, not a standalone decision-maker |
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- Validate outputs with domain experts |
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- Regularly update and fine-tune for specific use cases |
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- Be aware of potential contextual limitations |
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## How to Get Started with the Model |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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# Load BioLinkBERT model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained('biolinkbert-path') |
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tokenizer = AutoTokenizer.from_pretrained('biolinkbert-path') |
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# Example usage for text classification |
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def classify_biomedical_text(text): |
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inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512) |
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outputs = model(**inputs) |
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# Add specific classification logic based on your task |
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return outputs |
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``` |
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## Training Details |
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### Training Data |
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- **Dataset:** [Specific Biomedical Corpus - to be specified] |
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- **Domain:** Medical and Scientific Literature |
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- **Preprocessing:** [Specific preprocessing techniques] |
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### Training Procedure |
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#### Preprocessing |
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- Tokenization |
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- Text normalization |
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- Domain-specific preprocessing |
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#### Training Hyperparameters |
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- **Base Model:** BERT or BioBERT |
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- **Training Regime:** [Specific training details] |
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- **Precision:** [Training precision method] |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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- Held-out biomedical text corpus |
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- Diverse medical and scientific documents |
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#### Metrics |
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- Precision |
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- Recall |
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- F1-Score |
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- Domain-specific evaluation metrics |
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## Environmental Impact |
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- Estimated carbon emissions to be calculated |
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- Compute infrastructure details to be specified |
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## Technical Specifications |
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### Model Architecture |
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- **Base Architecture:** Transformer (BERT-like) |
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- **Specialized Domain:** Biomedical Text Processing |
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## Citation |
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**BibTeX:** |
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```bibtex |
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[To be added when research is published] |
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``` |
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**APA:** |
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[Citation details to be added] |
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## Glossary |
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- **NLP:** Natural Language Processing |
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- **BERT:** Bidirectional Encoder Representations from Transformers |
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- **Biomedical NLP:** Application of natural language processing techniques to medical and biological text |
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## More Information |
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For detailed information about the model's development, performance, and specific capabilities, please contact the model developers. |
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## Model Card Authors |
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[Names or affiliations of model card authors] |
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## Model Card Contact |
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[Contact information for further inquiries] |