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Hiner Original Xlm Roberta Large
H
Hiner Original Xlm Roberta Large
Developed by cfilt
This model is a named entity recognition (NER) model trained on the HiNER-original dataset based on the XLM-RoBERTa-large architecture, specifically designed for token classification tasks.
Downloads 56
Release Time : 5/1/2022
Model Overview
This is a token classification model for named entity recognition (NER), based on the XLM-RoBERTa-large architecture and trained on the HiNER-original dataset, capable of identifying specific entity categories in text.
Model Features
High-precision entity recognition
Achieves an F1 score of 89.2% on the HiNER-original dataset, demonstrating excellent performance.
Based on XLM-RoBERTa-large
Utilizes a powerful multilingual pre-trained model as its foundation, offering outstanding feature extraction capabilities.
End-to-end token classification
Directly processes raw text and outputs entity labels, simplifying the NER workflow.
Model Capabilities
Named Entity Recognition
Text Token Classification
Sequence Labeling
Use Cases
Information Extraction
News Entity Extraction
Identifies entities such as person names, locations, and organization names from news texts.
Helps in building knowledge graphs or event analysis.
Biomedical Text Analysis
Identifies disease, drug, and gene names in medical literature.
Assists in medical research and literature retrieval.
Text Processing
Document Automation Processing
Automatically labels key entities in contracts or legal documents.
Improves document processing efficiency and accuracy.
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