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Roberta Base Biomedical Clinical Es Finetuned Ner CRAFT
This model is a fine-tuned version of roberta-base-biomedical-clinical-es on the CRAFT dataset, designed for named entity recognition in biomedical clinical texts.
Downloads 17
Release Time : 3/2/2022
Model Overview
The model is specifically designed to identify six types of entities in biomedical clinical texts, including sequences, cells, proteins, genes, taxa, and chemicals.
Model Features
Biomedical Entity Recognition
Named entity recognition capability optimized specifically for biomedical clinical texts.
Multi-category Recognition
Capable of identifying six different types of biomedical entities.
High Accuracy
Achieves an accuracy of 0.9660 and an F1 score of 0.8200 on the evaluation set.
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.
Can identify key biomedical entities such as proteins and genes.
Clinical Record Analysis
Analyzes drug and chemical substance information in clinical records.
Accurately identifies chemical substances and drug names.
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