Distilbert Base Uncased Finetuned Ner
Named Entity Recognition (NER) model fine-tuned based on DistilBERT-base-uncased
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Release Time : 5/10/2022
Model Overview
This model is a fine-tuned version of DistilBERT-base-uncased, specifically optimized for Named Entity Recognition tasks. It retains the lightweight characteristics of the original model while being optimized for specific NER tasks.
Model Features
Lightweight and Efficient
Based on the DistilBERT architecture, it is 40% smaller than standard BERT models while retaining 95% of the performance.
Named Entity Recognition
Fine-tuned specifically for NER tasks, capable of identifying various named entities in text.
Model Capabilities
Text Entity Recognition
Sequence Labeling
Natural Language Processing
Use Cases
Information Extraction
News Entity Extraction
Identify entities such as person names, locations, and organizations from news texts.
Biomedical Text Analysis
Identify professional terms such as drug names, diseases, and gene names in medical literature.
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