Medical-NER
| Property | Value |
|---|---|
| Parameter Count | 184M |
| Model Type | Token Classification |
| Base Architecture | DeBERTa-v3 |
| License | MIT |
| Downloads | 197,639 |
What is Medical-NER?
Medical-NER is a specialized natural language processing model designed for medical named entity recognition. Built on Microsoft's DeBERTa-v3 architecture, this model has been fine-tuned on the PubMED dataset to recognize 41 distinct medical entities within text. With over 197,000 downloads, it has proven to be a valuable tool for medical text analysis.
Implementation Details
The model utilizes the DeBERTa-v3 architecture with native AMP (Automatic Mixed Precision) training. It was trained with carefully optimized hyperparameters including a learning rate of 2e-05, batch size of 16, and cosine learning rate scheduling with warmup.
- Trained over 30 epochs with Adam optimizer
- Implements gradient accumulation steps of 2
- Uses F32 tensor type for precise medical entity recognition
- Supports both pipeline and direct model loading approaches
Core Capabilities
- Recognition of 41 different medical entities
- Efficient processing of clinical text
- Support for batch processing
- Integration with Hugging Face's transformers library
- Deployment-ready with inference endpoints support
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful DeBERTa-v3 architecture with specialized medical entity recognition capabilities, making it particularly effective for processing clinical text and medical documentation. Its extensive training on PubMED data ensures high accuracy in medical context.
Q: What are the recommended use cases?
The model is ideal for clinical text analysis, medical record processing, research paper analysis, and any application requiring the identification of medical entities in text. It's particularly useful for healthcare systems, medical research, and clinical decision support systems.
