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Distilbert Base Uncased Finetuned Ner
A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset
Downloads 15
Release Time : 3/7/2022
Model Overview
This model is a lightweight version based on DistilBERT, specifically fine-tuned for Named Entity Recognition (NER) tasks. It can identify entities such as person names, locations, and organization names in text.
Model Features
Lightweight and Efficient
Based on the DistilBERT architecture, it is 40% smaller and 60% faster than the standard BERT model while maintaining 95% of its performance.
Professional NER Capability
Optimized specifically for Named Entity Recognition tasks, it can accurately identify entities such as person names, locations, and organization names in text.
Easy to Deploy
The model's small size makes it suitable for deployment and use in production environments.
Model Capabilities
Named Entity Recognition
Text Analysis
Information Extraction
Use Cases
Text Processing
News Entity Extraction
Extract key information such as person names, locations, and organization names from news articles.
Helps quickly build a knowledge graph for news.
Document Automation
Automatically identify key entities in contracts or legal documents.
Improves document processing efficiency and reduces manual labeling costs.
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