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Hiner Original Muril Base Cased
H
Hiner Original Muril Base Cased
Developed by cfilt
Hindi Named Entity Recognition model based on MuRIL architecture, trained on the HiNER-original dataset
Downloads 742
Release Time : 5/1/2022
Model Overview
This model is specifically designed for Hindi Named Entity Recognition (NER) tasks, capable of identifying entities such as person names, locations, and organizations in text.
Model Features
High-precision Hindi NER
Achieves an F1 score of 88.38% on the HiNER-original dataset
Based on MuRIL Architecture
Utilizes the MuRIL pre-trained model optimized for Indian languages
Model Capabilities
Hindi text processing
Named Entity Recognition
Entity classification
Use Cases
Natural Language Processing
Hindi Document Information Extraction
Extracts key information such as person names, locations, and organization names from Hindi documents
Can assist in building knowledge graphs or information retrieval systems
Hindi Text Analysis
Analyzes entity mentions in Hindi news or social media content
Useful for public opinion monitoring or content analysis
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