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Distilbert Base Uncased Finetuned Ner
A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset, suitable for English text entity recognition tasks.
Downloads 15
Release Time : 3/2/2022
Model Overview
This model is a fine-tuned version of DistilBERT, specifically designed for Named Entity Recognition (NER) tasks. It was trained on the English conll2003 dataset and can identify entities such as person names, locations, and organization names in text.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it is smaller and faster than the full BERT model while maintaining high accuracy.
High Accuracy
Achieves an F1 score of 0.9308 and an accuracy of 0.9838 on the conll2003 test set.
Ready-to-Use Model
Pre-fine-tuned for NER tasks and ready for direct deployment in production environments.
Model Capabilities
English text entity recognition
Identifying entities such as person names, locations, and organization names
Sequence labeling
Use Cases
Information Extraction
News Article Entity Extraction
Automatically identify and extract key entity information such as person names, locations, and organization names from news articles.
Helps in quickly building knowledge graphs or performing content analysis.
Document Processing
Legal Document Analysis
Automatically identify relevant entities in legal documents, such as party names and locations.
Improves the efficiency of legal document processing and reduces manual labeling costs.
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