test-ner Open Source Named Entity Recognition Model - Fine-tuned Based on BERT, with an F1 Score of 95.91% on the Evaluation Set

Test Ner

Developed by kSaluja

A BERT-based fine-tuned named entity recognition model achieving an F1 score of 95.91% on the evaluation set

Downloads 15

Release Time : 5/6/2022

Model Overview

This model is a BERT fine-tuned model for named entity recognition tasks, capable of identifying specific entity categories in text

High-precision recognition

Achieves 96.09% precision and 95.74% recall on the evaluation set

BERT-based architecture

Fine-tuned from the mature bert-base-uncased model with powerful semantic understanding capabilities

Linear learning rate scheduling

Optimizes training process with linear learning rate scheduling strategy

Text entity recognition

Named entity classification

Sequence labeling

Information extraction

News entity extraction

Identify entities such as people, locations, and organizations from news texts

Accurately tags various named entities in text

Biomedical text processing

Identify professional terms and entities in medical literature

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	151	0.1848	0.9060	0.9184	0.9122	0.9490
No log	2.0	302	0.1137	0.9548	0.9529	0.9538	0.9705
No log	3.0	453	0.1014	0.9609	0.9574	0.9591	0.9732

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