Ner Disease Ncbi Bionlp Bc5cdr Pubmed
Named entity recognition model trained on NCBI Disease dataset and BC5CDR dataset, specialized in identifying disease entities in biomedical literature
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Release Time : 3/2/2022
Model Overview
This model employs a PubMed pre-trained RoBERTa model for pre-training, specifically designed to recognize disease-related named entities from biomedical literature
Model Features
Biomedical Domain Optimization
Trained with PubMed pre-trained models and biomedical-specific datasets for better recognition performance on biomedical texts
Simplified Labeling System
Removes traditional BIO prefix labels and adopts a simplified entity annotation approach
Output Cleaning Function
Provides specialized output cleaning functions to optimize the coherence and readability of recognition results
Model Capabilities
Biomedical Text Analysis
Disease Entity Recognition
Named Entity Labeling
Use Cases
Biomedical Research
Literature Disease Entity Extraction
Automatically identify disease-related entities from biomedical literature
Accurately recognizes various disease names mentioned in the literature
Biomedical Database Construction
Assist in building biomedical knowledge graphs
Provides structured disease entity information for knowledge graphs
Medical Informatics
Clinical Record Analysis
Analyze disease-related information in clinical records
Helps extract key disease entities from clinical records
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