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Tner Xlm Roberta Large All English
A named entity recognition model fine-tuned based on XLM-RoBERTa, supporting entity recognition tasks in English text.
Downloads 5,023
Release Time : 3/2/2022
Model Overview
This model is a named entity recognition (NER) model fine-tuned on the XLM-RoBERTa architecture, specifically designed to identify named entities in English text.
Model Features
Multilingual Pretraining Foundation
Based on the XLM-RoBERTa-large architecture, it possesses strong cross-lingual representation capabilities.
Optimized for English Entity Recognition
Specifically fine-tuned for English text, optimizing named entity recognition performance.
Easy Integration
Can be easily integrated into existing NLP workflows via the Hugging Face Transformers library.
Model Capabilities
Identify named entities in text
Process English text
Label entity categories
Use Cases
Information Extraction
News Article Entity Extraction
Extract entities such as person names, locations, and organization names from news articles.
Structures news content for easier information retrieval and analysis.
Biomedical Literature Analysis
Identify drug, disease, and gene names in medical literature.
Assists in medical research and knowledge graph construction.
Content Classification
Social Media Content Analysis
Identify key entities in social media posts.
Useful for trend analysis and user interest mining.
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