---
library_name: transformers
tags:
- biomedical-nlp
- language-model
- bert
- medical-text
license: apache-2.0
language:
- en
base_model:
- michiyasunaga/BioLinkBERT-large
pipeline_tag: text-classification
---

# Model Card for BioLinkBERT

## Model Details

### Model Description

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.

- **Developed by:** [Research Institution/Team Name - to be specified]
- **Model type:** Transformer-based Biomedical Language Model
- **Language(s):** English (Biomedical Domain)
- **License:** [Specific License - to be added]
- **Finetuned from model:** Base BERT or BioBERT model

### Model Sources

- **Repository:** [GitHub/Model Repository Link]
- **Paper:** [Research Publication Link]
- **Demo:** [Optional Demo URL]

## Uses

### Direct Use

BioLinkBERT can be applied to various biomedical natural language processing tasks, including:
- Medical text classification
- Biomedical named entity recognition
- Scientific literature analysis
- Clinical document understanding

### Downstream Use

Potential applications include:
- Clinical decision support systems
- Biomedical research information extraction
- Medical literature summarization
- Semantic analysis of healthcare documents

### Out-of-Scope Use

- Not intended for direct medical diagnosis
- Performance may degrade outside biomedical domain
- Should not replace professional medical interpretation

## Bias, Risks, and Limitations

- Potential biases from training data
- Limited to biomedical text domains
- May not capture the most recent medical terminologies
- Requires careful validation in critical applications

### Recommendations

- Use as a supporting tool, not a standalone decision-maker
- Validate outputs with domain experts
- Regularly update and fine-tune for specific use cases
- Be aware of potential contextual limitations

## How to Get Started with the Model

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load BioLinkBERT model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained('biolinkbert-path')
tokenizer = AutoTokenizer.from_pretrained('biolinkbert-path')

# Example usage for text classification
def classify_biomedical_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    outputs = model(**inputs)
    # Add specific classification logic based on your task
    return outputs
```

## Training Details

### Training Data

- **Dataset:** [Specific Biomedical Corpus - to be specified]
- **Domain:** Medical and Scientific Literature
- **Preprocessing:** [Specific preprocessing techniques]

### Training Procedure

#### Preprocessing
- Tokenization
- Text normalization
- Domain-specific preprocessing

#### Training Hyperparameters
- **Base Model:** BERT or BioBERT
- **Training Regime:** [Specific training details]
- **Precision:** [Training precision method]

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data
- Held-out biomedical text corpus
- Diverse medical and scientific documents

#### Metrics
- Precision
- Recall
- F1-Score
- Domain-specific evaluation metrics

## Environmental Impact

- Estimated carbon emissions to be calculated
- Compute infrastructure details to be specified

## Technical Specifications

### Model Architecture
- **Base Architecture:** Transformer (BERT-like)
- **Specialized Domain:** Biomedical Text Processing

## Citation

**BibTeX:**
```bibtex
[To be added when research is published]
```

**APA:**
[Citation details to be added]

## Glossary

- **NLP:** Natural Language Processing
- **BERT:** Bidirectional Encoder Representations from Transformers
- **Biomedical NLP:** Application of natural language processing techniques to medical and biological text

## More Information

For detailed information about the model's development, performance, and specific capabilities, please contact the model developers.

## Model Card Authors

[Names or affiliations of model card authors]

## Model Card Contact

[Contact information for further inquiries]