tf-xlm-r-ner-40-lang
| Property | Value |
|---|---|
| Base Model | XLM-RoBERTa-base |
| Task | Named Entity Recognition |
| Languages | 40 languages |
| Author | jplu |
| Framework | TensorFlow |
What is tf-xlm-r-ner-40-lang?
tf-xlm-r-ner-40-lang is a multilingual Named Entity Recognition (NER) model built on XLM-RoBERTa base architecture. It's specifically fine-tuned to identify three types of entities (Location, Organization, and Person) across 40 different languages from the Wikiann dataset. The model achieves an impressive average F1-score of 0.87 across all supported languages.
Implementation Details
The model is implemented using TensorFlow and leverages the XLM-RoBERTa architecture for multilingual understanding. It processes text sequences up to 128 tokens and has been trained using the AdamW optimizer with specific configurations for token classification tasks.
- Supports major world languages including English, Chinese, Arabic, Russian, and many more
- Achieves high performance across different writing systems and language families
- Trained on the XTREME benchmark dataset
- Uses fast tokenization for improved processing speed
Core Capabilities
- Named Entity Recognition for LOC (Location), ORG (Organization), and PER (Person) entities
- Average precision of 0.86 and recall of 0.87 across all languages
- Particularly strong performance in European languages (90%+ F1 score)
- Handles both high-resource and low-resource languages effectively
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to perform NER across 40 languages with consistent performance makes it unique. It maintains high accuracy even for languages with different scripts and grammatical structures, making it a versatile choice for multilingual applications.
Q: What are the recommended use cases?
This model is ideal for multilingual information extraction, cross-lingual entity analysis, and global text processing applications. It's particularly useful for organizations dealing with content in multiple languages needing consistent entity recognition.
