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Roberta Finetuned Ner
R
Roberta Finetuned Ner
Developed by kSaluja
Named Entity Recognition (NER) model fine-tuned based on xlm-roberta-base, demonstrating excellent performance on the evaluation set (F1 score 0.9777)
Downloads 25
Release Time : 3/15/2022
Model Overview
This model is a named entity recognition model fine-tuned based on the xlm-roberta-base architecture, suitable for identifying specific categories of named entities from text.
Model Features
High-precision recognition
Achieves an F1 score of 97.77% on the evaluation set, demonstrating outstanding performance
Based on RoBERTa architecture
Utilizes the powerful xlm-roberta-base as the base model, with excellent text comprehension capabilities
Multilingual potential
Based on the xlm-roberta architecture, potentially supports multilingual named entity recognition (requires verification)
Model Capabilities
Text entity recognition
Sequence labeling
Multi-category entity classification
Use Cases
Information extraction
News entity extraction
Identify entities such as person names, locations, and organizations from news text
High-precision extraction of various named entities
Biomedical text processing
Identify professional terms such as diseases, drugs, and genes in medical literature
Data labeling
Automated text annotation
Automatically generate entity annotations for NLP training data
Reduce manual labeling workload
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