deberta-base-fine-tuned-ner Open-Source Model - Precision Identification of Named Entities in Text

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Deberta Base Fine Tuned Ner

Developed by geckos

Named Entity Recognition (NER) model fine-tuned on the conll2003 dataset based on DeBERTa-base

Sequence Labeling

Transformers

Open Source License:MIT #High-precision NER #DeBERTa architecture #CoNLL2003 fine-tuning

Downloads 456

Release Time : 3/2/2022

Model Overview

This model is specifically designed for named entity recognition tasks, performing exceptionally well on the CoNLL-2003 dataset, capable of accurately identifying entities such as person names, locations, and organizations in text

Model Features

High-precision entity recognition

Achieves an F1 score of 96.08% on the CoNLL-2003 test set, demonstrating excellent performance

Based on DeBERTa architecture

Utilizes an improved Transformer architecture with enhanced contextual understanding

End-to-end training

Directly fine-tuned on NER tasks without requiring additional feature engineering

Model Capabilities

Named Entity Recognition

Text token classification

Entity boundary detection

Use Cases

Information extraction

News text analysis

Extract person names, locations, and organization names from news articles

Accurately identifies various entities with an F1 score of 96.08%

Document processing

Automatically annotate key entity information in documents

Knowledge graph construction

Knowledge graph entity extraction

Extract entities from unstructured text for knowledge graph construction

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1419	1.0	878	0.0628	0.9290	0.9288	0.9289	0.9835
0.0379	2.0	1756	0.0466	0.9456	0.9567	0.9511	0.9878
0.0176	3.0	2634	0.0473	0.9539	0.9575	0.9557	0.9890
0.0098	4.0	3512	0.0468	0.9570	0.9635	0.9603	0.9896
0.0043	5.0	4390	0.0501	0.9563	0.9652	0.9608	0.9899

Property	Details
Model Type	Fine - tuned DeBERTa - base for NER
Training Data	conll2003
Metrics	Precision, Recall, F1, Accuracy

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Deberta Base Fine Tuned Ner

Model Overview

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🚀 DeBERTa-base Finetuned for NER

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