bert-tiny-finetuned-ner Open-source Named Entity Recognition Model - Accurately Identify Text Entity Information

Bert Tiny Finetuned Ner

Developed by gagan3012

This model is a Named Entity Recognition (NER) model fine-tuned on the CoNLL2003 dataset based on BERT-tiny

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Release Time : 3/2/2022

Model Overview

A fine-tuned model for Named Entity Recognition tasks, based on the BERT-tiny architecture and trained on the CoNLL2003 dataset

Efficient and Lightweight

Based on the BERT-tiny architecture with fewer model parameters, suitable for resource-constrained environments

High Accuracy

Achieves an accuracy of 0.96 on the CoNLL2003 test set

Balanced Performance

Balanced precision (0.808) and recall (0.827) with an F1 score of 0.818

Named Entity Recognition

Text Token Classification

Information Extraction

News Entity Recognition

Identify entities such as person names, locations, and organization names from news texts

Document Structuring

Extract key entities from unstructured documents into structured data

Task Name	Dataset	Precision	Recall	F1	Accuracy
Token Classification	conll2003	0.8083060109289617	0.8273856136033113	0.8177345348001547	0.9597597979252387

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0355	1.0	878	0.1692	0.8072	0.8248	0.8159	0.9594
0.0411	2.0	1756	0.1678	0.8101	0.8277	0.8188	0.9600
0.0386	3.0	2634	0.1697	0.8103	0.8269	0.8186	0.9599
0.0373	4.0	3512	0.1694	0.8106	0.8263	0.8183	0.9600
0.0383	5.0	4390	0.1689	0.8083	0.8274	0.8177	0.9598

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