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Biobert Genetic Ner
B
Biobert Genetic Ner
Developed by alvaroalon2
This model is based on the BioBERT architecture, fine-tuned for named entity recognition tasks in the biomedical field, with a special focus on genetics-related entity recognition.
Downloads 3,269
Release Time : 3/2/2022
Model Overview
Fine-tuned on the JNLPBA and BC2GM corpora, this model is suitable for biomedical named entity recognition (BioNER) and biomedical named entity normalization (BioNEN) tasks, particularly excelling in identifying genetics-related entities.
Model Features
Biomedical Domain Optimization
Optimized for biomedical texts, especially proficient in handling genetics-related terms and entities.
Multi-dataset Fine-tuning
Fine-tuned on both the JNLPBA and BC2GM authoritative biomedical corpora to enhance model generalization.
Efficient Entity Recognition
Capable of accurately identifying various entities in biomedical texts, particularly genetics-related entities.
Model Capabilities
Biomedical Text Analysis
Named Entity Recognition
Genetics Terminology Recognition
Use Cases
Biomedical Research
Literature Entity Extraction
Extract genetics-related entities such as genes and proteins from biomedical research papers
Improves efficiency in literature information extraction
Clinical Record Analysis
Analyze genetic disease-related information in electronic health records
Supports clinical decision-making
Bioinformatics
Database Annotation
Automatically annotate entity information in biomedical databases
Reduces manual annotation workload
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