OpenMed-NER-GenomicDetect-ElectraMed-335M EU AI Act Compliance Profile

🧬 OpenMed-NER-GenomicDetect-ElectraMed-335M

Specialized model for Gene Entity Recognition - Gene-related entities

📋 Model Overview

This model is a state-of-the-art fine-tuned transformer engineered to deliver enterprise-grade accuracy for gene entity recognition - gene-related entities. 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 GELLUS 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-Cell-line-name
• I-Cell-line-name

📊 Dataset

Gellus corpus targets gene recognition and genetics entities for genomics and molecular biology applications.

The Gellus corpus is a biomedical NER dataset specifically designed for gene recognition and genetics entity extraction in molecular biology literature. This corpus contains comprehensive annotations for gene names, genetic variants, and genomics-related entities

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