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Distilbert Base Uncased Finetuned Ner
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Distilbert Base Uncased Finetuned Ner

Developed by suwani
A lightweight model based on DistilBERT, specifically fine-tuned for Named Entity Recognition (NER) tasks
Sequence Labeling
Transformers
Open Source License:Apache-2.0#Named Entity Recognition#Lightweight BERT#English Text Processing
Downloads 16
Release Time : 3/2/2022

Model Overview

This model is a fine-tuned version of distilbert-base-uncased for Named Entity Recognition tasks, featuring high inference efficiency and moderate accuracy

Model Features

Efficient Inference
Utilizes DistilBERT architecture, 40% smaller than standard BERT while retaining 95% of its performance
NER Optimization
Specifically fine-tuned for Named Entity Recognition tasks, performing well on relevant metrics
Lightweight
Compact model size, suitable for deployment in resource-constrained environments

Model Capabilities

Text Entity Recognition
Named Entity Classification
Sequence Labeling

Use Cases

Information Extraction
News Entity Extraction
Identify entities such as person names, locations, and organizations from news texts
F1 score reaches 0.6655
Biomedical Text Analysis
Identify disease, drug, and gene names in medical literature
Document Processing
Contract Parsing
Automatically identify key clauses and parties in contracts
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