distilbert-base-uncased-finetuned-ner-final Open-source Model - Lightweight and Precise Recognition of Specific Named Entities

Home

Distilbert Base Uncased Finetuned Ner Final

Developed by Lilya

A lightweight Named Entity Recognition (NER) model based on the DistilBERT architecture, fine-tuned for specific tasks

Sequence Labeling

Transformers

#Named Entity Recognition #Lightweight BERT #English Text Processing

Downloads 15

Release Time : 4/27/2022

Model Overview

This model is a lightweight version based on the DistilBERT architecture, specifically fine-tuned for Named Entity Recognition tasks. It inherits the efficiency of DistilBERT while being optimized for NER tasks.

Model Features

Lightweight and Efficient

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

Task-Specific Optimization

Specifically fine-tuned for Named Entity Recognition tasks

English Language Support

Focused on entity recognition for English text

Model Capabilities

Text Entity Recognition

Named Entity Classification

Sequence Labeling

Use Cases

Information Extraction

News Entity Extraction

Identify entities such as people, places, and organizations from news articles

Document Analysis

Automatically label key entity information in documents

Knowledge Graph Construction

Knowledge Graph Entity Extraction

Extract entities from text for building knowledge graphs

Property	Details
Model Name	distilbert-base-uncased-finetuned-ner-final

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご

Distilbert Base Uncased Finetuned Ner Final

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 distilbert-base-uncased-finetuned-ner-final

🚀 Quick Start

📚 Documentation

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Framework versions