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Distilbert Base Uncased Finetuned Ner
A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset, featuring efficient inference performance and high accuracy.
Downloads 16
Release Time : 3/2/2022
Model Overview
This model is a lightweight version based on DistilBERT, specifically designed for Named Entity Recognition (NER) tasks. After fine-tuning on the conll2003 dataset, it can identify entities such as person names, locations, and organization names in text.
Model Features
Efficient Inference
As a DistilBERT model, it is 40% smaller and 60% faster in inference than standard BERT while maintaining over 95% accuracy.
High Accuracy
Achieves an F1 score of 93.04% on the conll2003 test set, demonstrating excellent entity recognition capability.
Lightweight
A knowledge-distilled lightweight model suitable for deployment in resource-constrained environments.
Model Capabilities
Named Entity Recognition
Text Token Classification
English Text Processing
Use Cases
Information Extraction
News Entity Extraction
Extract key information such as person names, locations, and organization names from news texts.
Can accurately identify over 93% of named entities.
Document Automation Processing
Automatically process named entities in legal or medical documents.
Knowledge Graph Construction
Knowledge Graph Entity Recognition
Provide foundational entity recognition capabilities for knowledge graph construction.
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