roberta-ner-multilingual
| Property | Value |
|---|---|
| Parameter Count | 559M |
| License | MIT |
| Languages Supported | 21 |
| Research Paper | View Paper |
| F1 Score | 88.26% |
What is roberta-ner-multilingual?
roberta-ner-multilingual is a powerful multilingual Named Entity Recognition (NER) model based on the XLM-RoBERTa architecture. It's designed to identify and classify named entities (persons, organizations, and locations) across 21 different languages, making it a versatile tool for multilingual text analysis.
Implementation Details
The model was fine-tuned on the WikiANN dataset, utilizing 375,100 training sentences and validated on 173,100 examples. It implements the IOB tagging format for entity classification and is built upon the XLM-RoBERTa architecture, which was pre-trained on 2.5TB of filtered CommonCrawl data.
- Supports entity detection for PER (Person), ORG (Organization), and LOC (Location)
- Achieves 90% F1 score for Location detection
- 91.15% F1 score for Person detection
- 82.91% F1 score for Organization detection
Core Capabilities
- Multilingual support for 21 languages including English, German, French, Chinese, and more
- High accuracy with 93.98% overall accuracy
- Efficient token classification using the IOB2 format
- Easy integration with HuggingFace Transformers library
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle 21 different languages while maintaining high accuracy (88.26% F1 score) makes it particularly valuable for multilingual NER tasks. It's built on the robust XLM-RoBERTa architecture and fine-tuned specifically for named entity recognition.
Q: What are the recommended use cases?
This model is ideal for multilingual information extraction, document analysis, and entity recognition in various languages. It's particularly useful for applications requiring cross-lingual entity detection in news articles, academic texts, and general content analysis.
