distilbert-base-uncased-finetuned-ner Open-source Named Entity Recognition Model - Lightweight and Precise Identification of Entity Information

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Distilbert Base Uncased Finetuned Ner

Developed by lucasmtz

A lightweight named entity recognition model based on DistilBERT, fine-tuned on the conll2003 dataset

Sequence Labeling

Transformers

Open Source License:Apache-2.0 #Named Entity Recognition #High-precision NER #Lightweight BERT

Downloads 14

Release Time : 3/2/2022

Model Overview

This model is a lightweight version based on DistilBERT, specifically fine-tuned for named entity recognition tasks. It performs excellently on the CoNLL-2003 dataset and is suitable for entity labeling tasks in English text.

Model Features

Efficient and Lightweight

Based on the DistilBERT architecture, it is smaller and faster than the standard BERT model while maintaining good performance.

High-precision NER

Achieves an F1 score of 93.1% on the CoNLL-2003 test set, demonstrating excellent performance.

Fast Inference

The distilled architecture design enables faster model inference, making it suitable for production environment deployment.

Model Capabilities

Named Entity Recognition

Text Token Classification

Entity Labeling

Use Cases

Information Extraction

News Entity Extraction

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

Accuracy 98.3%, F1 score 93.1%

Biomedical Text Processing

Identify professional terms and entities in medical literature.

Data Preprocessing

Knowledge Graph Construction

Provide entity recognition preprocessing for knowledge graph construction.

Property	Details
Model Type	Fine - tuned version of distilbert-base-uncased
Training Data	conll2003
Metrics	precision, recall, f1, accuracy

Task	Dataset	Metrics	Value
Token Classification	conll2003	Precision	0.9252181597260577
Token Classification	conll2003	Recall	0.9370175634858485
Token Classification	conll2003	F1	0.9310804802134283
Token Classification	conll2003	Accuracy	0.9834146186474335

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.244	1.0	878	0.0714	0.9104	0.9181	0.9142	0.9797
0.0568	2.0	1756	0.0605	0.9183	0.9351	0.9266	0.9827
0.0302	3.0	2634	0.0610	0.9252	0.9370	0.9311	0.9834

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Distilbert Base Uncased Finetuned Ner

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