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Bert Finetuned Ner

Developed by fundrais123
A named entity recognition model fine-tuned on the CoNLL2003 dataset based on BERT-base-cased
Downloads 23
Release Time : 9/11/2023

Model Overview

This model is a BERT model optimized for named entity recognition tasks, achieving outstanding performance on the CoNLL2003 dataset with an F1 score of 0.9412.

Model Features

High-precision Named Entity Recognition
Achieves a precision of 0.9326 and recall of 0.9500 on the CoNLL2003 validation set
Powerful Feature Extraction Based on BERT
Utilizes the BERT-base-cased pre-trained model as a foundation, offering strong contextual understanding capabilities
Lightweight Fine-tuning
Requires only 3 training epochs to achieve excellent performance

Model Capabilities

Named Entity Recognition
Text Token Classification
Entity Boundary Detection

Use Cases

Information Extraction
News Entity Extraction
Identify entities such as person names, locations, and organization names from news texts
F1 score reaches 0.9412
Document Analysis
Automatically tag key entity information in documents
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