51-languages-classifier
| Property | Value |
|---|---|
| Author | qanastek |
| Base Architecture | XLM-RoBERTa |
| License | cc-by-4.0 |
| Paper | Unsupervised Cross-lingual Representation Learning at Scale |
What is 51-languages-classifier?
The 51-languages-classifier is a sophisticated multilingual text classification model built on XLM-RoBERTa architecture. It's designed to identify and classify text across 51 different languages with remarkable accuracy, achieving an average F1-score of 98.89%. The model was trained on the MASSIVE dataset, which contains over 1 million utterances spanning various languages and intents.
Implementation Details
The model leverages the XLM-RoBERTa base architecture and can be easily implemented using the Hugging Face Transformers library. It processes text input and returns the detected language along with a confidence score. The model supports a wide range of languages from major ones like English, Chinese, and Arabic to less common ones like Welsh and Javanese.
- Built on XLM-RoBERTa architecture for robust cross-lingual understanding
- Trained on MASSIVE dataset with 1M+ annotated utterances
- Supports 51 languages with country-specific variants
- Simple integration through Hugging Face Transformers pipeline
Core Capabilities
- High-accuracy language identification (98.89% average accuracy)
- Support for both common and rare languages
- Handles various writing systems (Latin, Cyrillic, Chinese characters, etc.)
- Confidence scoring for predictions
- Efficient processing of single-shot interactions
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to accurately classify 51 different languages with extremely high precision (many languages achieving 99%+ accuracy) makes it stand out. It's particularly noteworthy for including less-commonly supported languages and regional variants.
Q: What are the recommended use cases?
The model is ideal for language detection in multilingual applications, content classification systems, automated language routing in customer service, and any scenario requiring reliable language identification across a diverse range of languages.
