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Biobert Base Cased V1.2 Finetuned Ner CRAFT
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Biobert Base Cased V1.2 Finetuned Ner CRAFT

Developed by StivenLancheros
A named entity recognition model fine-tuned on the CRAFT corpus based on BioBERT, used to identify 6 types of entities in biomedical texts
Sequence Labeling
Transformers
#Biomedical NER#High-precision entity recognition#CRAFT corpus
Downloads 15
Release Time : 3/11/2022

Model Overview

This model is specifically designed for processing biomedical texts and can identify 6 types of entity labels including sequences, cells, proteins, genes, taxa, and chemicals.

Model Features

Biomedical domain optimization
Based on the BioBERT pre-trained model, specifically optimized for biomedical texts
Multi-category entity recognition
Can simultaneously identify 6 different types of biomedical entities
High-precision recognition
Achieves an F1 score of 0.8382 on the CRAFT dataset

Model Capabilities

Biomedical text analysis
Named entity recognition
Sequence labeling

Use Cases

Biomedical research
Literature information extraction
Automatically extracts key entity information from biomedical literature
Helps researchers quickly locate key biomedical entities in literature
Knowledge graph construction
Automatically annotates entities for biomedical knowledge graphs
Improves the efficiency and accuracy of knowledge graph construction
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