distilbert-base-uncased-finetuned-ner Open-source Model - Lightweight and Free for Efficient Named Entity Recognition

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Distilbert Base Uncased Finetuned Ner

Developed by srosy

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 Accuracy #Lightweight BERT

Downloads 16

Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of DistilBERT, specifically designed for Named Entity Recognition (NER) tasks, demonstrating excellent performance on the conll2003 dataset.

Model Features

Efficient and Lightweight

Based on the DistilBERT architecture, it is smaller and faster than standard BERT models while maintaining high performance

High Accuracy

Achieves 98.44% accuracy and 93.23% F1 score on the conll2003 dataset

Fast Inference

The distilled model design enables faster inference speed compared to the full BERT model

Model Capabilities

Named Entity Recognition

Text Token Classification

Entity Extraction

Use Cases

Natural Language Processing

News Entity Extraction

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

High accuracy in recognizing various named entities

Document Information Extraction

Extract key entity information from legal or business documents

Can help automate document processing workflows

Property	Details
Tags	generated_from_trainer
Datasets	conll2003
Metrics	precision, recall, f1, accuracy

Model Name	Task	Dataset	Metric	Value
distilbert-base-uncased-finetuned-ner	Token Classification	conll2003	Accuracy	0.9844313470062116

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0616	1.0	878	0.0604	0.9195	0.9370	0.9282	0.9833
0.0328	2.0	1756	0.0588	0.9258	0.9375	0.9316	0.9841
0.0246	3.0	2634	0.0590	0.9266	0.9381	0.9323	0.9844

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Distilbert Base Uncased Finetuned Ner

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