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Typo Detector Distilbert En

Developed by m3hrdadfi
A spelling error detection model based on the DistilBERT architecture, used to identify spelling errors in text
Downloads 25.05k
Release Time : 3/2/2022

Model Overview

This model is a Named Entity Recognition (NER) model based on DistilBERT, specifically designed to detect spelling errors in text. It is trained using the NeuSpell corpus and can efficiently and accurately identify spelling issues in text.

Model Features

High accuracy
The model achieves an F1 score of 0.989 in spelling error detection tasks
Based on DistilBERT
Uses the lightweight DistilBERT architecture, reducing computational resource requirements while maintaining performance
Easy to use
Can be easily integrated into applications via Transformers pipelines

Model Capabilities

Text spelling error detection
Named Entity Recognition

Use Cases

Text editing and proofreading
Document proofreading
Automatically detect spelling errors in documents
Improves document quality and professionalism
Content moderation
Identify spelling issues in user-generated content
Enhances platform content quality
Education
Language learning assistance
Helps language learners identify spelling errors in writing
Improves learning efficiency
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