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Distilbert Base Uncased Finetuned Ner
A lightweight named entity recognition model based on DistilBERT, fine-tuned on an unknown dataset, featuring efficient inference speed and good entity recognition capability.
Downloads 17
Release Time : 3/2/2022
Model Overview
This model is a fine-tuned named entity recognition model based on the lightweight version of DistilBERT, suitable for entity recognition tasks in English text.
Model Features
Lightweight and Efficient
Based on the DistilBERT architecture, it is smaller and faster than the standard BERT model while maintaining good performance.
High Accuracy
Achieves an accuracy of 0.9831 and an F1 score of 0.9290 on the evaluation set, demonstrating excellent performance.
Balanced Performance
Precision (0.9229) and recall (0.9352) are well-balanced without significant bias.
Model Capabilities
English text entity recognition
Efficient inference
Sequence labeling
Use Cases
Information Extraction
News Article Entity Recognition
Extract entity information such as person names, locations, and organization names from news articles.
Can accurately identify various named entities in the text.
Biomedical Literature Analysis
Identify disease, drug, and gene names in medical literature.
Business Intelligence
Customer Feedback Analysis
Extract product names and issue entities from customer feedback.
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