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Biobert V1.1 Pubmed Finetuned Ner
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Biobert V1.1 Pubmed Finetuned Ner

Developed by fidukm34
Named entity recognition model fine-tuned on the NCBI disease dataset based on BioBERT
Sequence Labeling
Transformers
#Biomedical NER#PubMed Optimization#High Accuracy
Downloads 197
Release Time : 3/2/2022

Model Overview

This model is based on the BioBERT architecture, pre-trained on PubMed literature, and fine-tuned for the NCBI disease dataset, specifically designed for disease entity recognition tasks in the biomedical field.

Model Features

Biomedical Domain Optimization
Pre-trained on PubMed literature, making it particularly suitable for processing biomedical texts
High Accuracy
Achieves 98.27% accuracy on the NCBI disease dataset
Fine-grained Entity Recognition
Accurately identifies disease-related entities in biomedical literature

Model Capabilities

Biomedical Text Analysis
Disease Entity Recognition
Scientific Literature Information Extraction

Use Cases

Medical Research
Literature Disease Entity Extraction
Automatically identifies and extracts disease-related entities from medical research literature
F1 score reaches 0.8625
Bioinformatics
Disease Knowledge Graph Construction
Provides entity recognition support for building disease knowledge graphs
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