xlm-roberta-base-language-detection
| Property | Value |
|---|---|
| Parameter Count | 278M |
| License | MIT |
| Paper | View Paper |
| Supported Languages | 20 |
| Accuracy | 99.6% |
What is xlm-roberta-base-language-detection?
This is a fine-tuned version of XLM-RoBERTa specifically designed for language detection across 20 different languages. Built on the xlm-roberta-base architecture, it combines advanced transformer technology with a classification head to achieve state-of-the-art language identification accuracy.
Implementation Details
The model utilizes a transformer architecture with 278M parameters and has been fine-tuned on a specialized Language Identification dataset containing 70,000 training samples. It employs PyTorch framework and includes safetensors support for improved efficiency.
- Training performed using Adam optimizer with learning rate 2e-05
- Batch size: 64 (training) / 128 (evaluation)
- Two-epoch training with linear learning rate scheduler
- Native AMP mixed precision training
Core Capabilities
- Supports 20 languages including Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Italian, Japanese, Dutch, Polish, Portuguese, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, and Chinese
- Achieves 99.6% accuracy on test set, outperforming baseline langid library (98.5%)
- Provides confidence scores for language predictions
- Easily integrable through Hugging Face's pipeline API
Frequently Asked Questions
Q: What makes this model unique?
The model combines XLM-RoBERTa's robust multilingual understanding with specialized language detection training, achieving exceptional accuracy across 20 languages while maintaining practical deployment capabilities.
Q: What are the recommended use cases?
This model is ideal for automated language detection in content management systems, multilingual document processing, and language-specific routing in NLP pipelines. It's particularly effective for applications requiring high-confidence language identification across multiple languages.
