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Codeswitch Hineng Ner Lince
C
Codeswitch Hineng Ner Lince
Developed by sagorsarker
This is a pre-trained named entity recognition model specifically designed for Hindi-English code-mixed data, trained on the LinCE dataset.
Downloads 53
Release Time : 3/2/2022
Model Overview
This model is used for named entity recognition tasks in Hindi and English mixed texts, suitable for natural language processing applications in code-switching scenarios.
Model Features
Code-mixed Processing
Specially optimized for Hindi-English mixed texts, effectively handling code-switching scenarios
Based on LinCE Dataset
Trained using the standard LinCE code-switching dataset, ensuring reliable benchmark performance
Easy Integration
Provides a dedicated Python package (codeswitch) and two calling methods for quick integration into applications
Model Capabilities
Hindi-English mixed text processing
Named Entity Recognition
Code-switching analysis
Use Cases
Natural Language Processing
Social Media Text Analysis
Analyzing named entities in Hindi-English mixed texts commonly found on social media in India
Can identify entities such as person names, locations, and organizations in mixed texts
Multilingual Chatbots
Enhancing chatbots' understanding of mixed-language inputs from Indian users
Improves accuracy in recognizing entities in user inputs
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