Distilbert-base-uncased-finetuned-ner Open-source Named Entity Recognition Model

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Distilbert Base Uncased Finetuned Ner

Developed by chancar

Named Entity Recognition (NER) model fine-tuned based on DistilBERT-base-uncased

Sequence Labeling

Transformers

Open Source License:Apache-2.0 #Named Entity Recognition #Lightweight BERT #English Text Processing

Downloads 15

Release Time : 5/10/2022

Model Overview

This model is a fine-tuned version of DistilBERT-base-uncased, specifically optimized for Named Entity Recognition tasks. It retains the lightweight characteristics of the original model while being optimized for specific NER tasks.

Model Features

Lightweight and Efficient

Based on the DistilBERT architecture, it is 40% smaller than standard BERT models while retaining 95% of the performance.

Named Entity Recognition

Fine-tuned specifically for NER tasks, capable of identifying various named entities in text.

Model Capabilities

Text Entity Recognition

Sequence Labeling

Natural Language Processing

Use Cases

Information Extraction

News Entity Extraction

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

Biomedical Text Analysis

Identify professional terms such as drug names, diseases, and gene names in medical literature.

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.9591	1.0	878	0.9780	0.0	0.0	0.0	0.7891

Property	Details
Model Type	distilbert - base - uncased - finetuned - ner
Metrics	precision, recall, f1, accuracy
License	apache - 2.0

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Distilbert Base Uncased Finetuned Ner

Model Overview

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