Distilbert Base Uncased Finetuned Ner
A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset
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Release Time : 6/5/2022
Model Overview
This model is a lightweight named entity recognition model based on the DistilBERT architecture, specifically designed for token classification tasks, and performs excellently after fine-tuning on the conll2003 dataset.
Model Features
Lightweight and Efficient
Based on the DistilBERT architecture, it is more lightweight than standard BERT models while maintaining high performance.
High Accuracy
Achieves 97.17% accuracy and 87.4% F1 score on the conll2003 dataset.
Fast Training
Only requires 3 epochs of training to achieve good performance.
Model Capabilities
Named Entity Recognition
Text Token Classification
Use Cases
Natural Language Processing
News Entity Extraction
Identify entities such as person names, locations, and organization names from news texts.
97.17% accuracy, 87.4% F1 score
Document Information Extraction
Extract key entity information from business documents.
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