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Distilbert Base Uncased Finetuned Ner
A named entity recognition model fine-tuned on the conll2003 dataset based on the DistilBERT-base-uncased model, excelling in NER tasks.
Downloads 947
Release Time : 4/11/2022
Model Overview
This model is a lightweight version of DistilBERT, specifically fine-tuned for named entity recognition tasks, capable of efficiently identifying named entities in text.
Model Features
Efficient and Lightweight
Based on the DistilBERT architecture, it is smaller and faster than standard BERT models while maintaining high performance.
High Accuracy
Achieves an F1 score of 0.979 on the conll2003 test set, demonstrating excellent performance.
Fast Inference
The distilled model design enables faster inference, making it suitable for production environment deployment.
Model Capabilities
Named Entity Recognition
Text Token Classification
Entity Boundary Detection
Entity Type Classification
Use Cases
Information Extraction
News Entity Extraction
Extract entities such as person names, locations, and organization names from news text.
High accuracy in recognizing various named entities.
Document Analysis
Process professional terms and entities in legal or medical documents.
Effectively identifies domain-specific entities.
Data Preprocessing
Search Engine Optimization
Preprocess and tag key entities in text for search systems.
Improves search relevance and accuracy.
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