OpenMed-NER-DiseaseDetect-TinyMed-66M EU AI Act Compliance Profile

🧬 OpenMed-NER-DiseaseDetect-TinyMed-66M

Specialized model for Disease Entity Recognition - Disease entities from the BC5CDR dataset

📋 Model Overview

This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for disease entity recognition - disease entities from the bc5cdr dataset. This specialized model excels at identifying and extracting biomedical entities from clinical texts, research papers, and healthcare documents, enabling applications such as drug interaction detection, medication extraction from patient records, adverse event monitoring, literature mining for drug discovery, and biomedical knowledge graph construction with production-ready reliability for clinical and research applications.

🎯 Key Features

• High Precision: Optimized for biomedical entity recognition
• Domain-Specific: Trained on curated BC5CDR_DISEASE dataset
• Production-Ready: Validated on clinical benchmarks
• Easy Integration: Compatible with Hugging Face Transformers ecosystem

🏷️ Supported Entity Types

This model can identify and classify the following biomedical entities:

• B-DISEASE
• I-DISEASE

📊 Dataset

BC5CDR-Disease targets disease entity recognition from the BioCreative V Chemical-Disease Relation extraction corpus.

The BC5CDR-Disease corpus is the disease-focused component of the BioCreative V Chemical-Disease Relation (CDR) task, containing 1,500 PubMed abstracts with 5,818 annotated disease entities. This manually curated dataset is de

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