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Distilbert Base Uncased Finetuned Ner

Developed by chancar
Named Entity Recognition (NER) model fine-tuned based on DistilBERT-base-uncased
Downloads 15
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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