Distilroberta Base Ner Wikiann Conll2003 4 Class
A named entity recognition model based on DistilRoBERTa-base, fine-tuned on the wikiann and conll2003 datasets, supporting 4-class entity recognition.
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Release Time : 3/2/2022
Model Overview
This model is designed for Named Entity Recognition (NER) tasks, capable of identifying person names (PER), organization names (ORG), location names (LOC), and miscellaneous entities (MISC) in text.
Model Features
High-precision entity recognition
Achieves an F1 score of 95.39 on the combined dataset, demonstrating excellent performance.
Multi-category support
Supports recognition of 4 entity types: person names (PER), organization names (ORG), location names (LOC), and miscellaneous entities (MISC).
Efficient model
Based on DistilRoBERTa-base, it reduces model size while maintaining performance.
Model Capabilities
Text entity recognition
Multi-category entity classification
Use Cases
Information extraction
News text analysis
Extract key information such as person names, organization names, and location names from news articles.
Can accurately identify over 90% of entities
Document processing
Automatically process and analyze named entities in documents.
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