biomedical-ner-all
| Property | Value |
|---|---|
| Parameter Count | 66.4M |
| License | Apache 2.0 |
| Training Dataset | Maccrobat |
| Carbon Footprint | 0.028 kg CO2 |
| Training Time | 30.17 minutes |
What is biomedical-ner-all?
biomedical-ner-all is a specialized Named Entity Recognition (NER) model designed for biomedical text analysis. Built on the DistilBERT architecture, this model can identify and classify 107 different types of biomedical entities from clinical texts and case reports. The model was developed by Deepak John Reji and Shaina Raza as part of their research in AI applications for biomedicine.
Implementation Details
The model is implemented using the Transformers library and PyTorch backend, utilizing the DistilBERT base architecture fine-tuned on the Maccrobat dataset. It operates with 32-bit floating-point precision and has been optimized for efficient inference.
- Architecture: DistilBERT-based model with token classification head
- Training Infrastructure: Single GeForce RTX 3060 Laptop GPU
- Integration: Supports HuggingFace pipeline API for easy deployment
Core Capabilities
- Recognition of 107 distinct biomedical entity types
- Processing of clinical case reports and medical documentation
- Support for both CPU and GPU inference
- Efficient processing with optimized model size
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive coverage of biomedical entities (107 types) while maintaining efficiency through the DistilBERT architecture. Its training on the Maccrobat dataset ensures relevance to real-world medical documentation.
Q: What are the recommended use cases?
The model is ideal for medical text analysis, clinical research, automated medical record processing, and biomedical information extraction. It's particularly useful for identifying medical conditions, treatments, and biological entities in clinical case reports.
