Language Detection Model
| Property | Value |
|---|---|
| Base Model | XLM-RoBERTa |
| Accuracy | 99.6% |
| Supported Languages | 20 |
| Training Dataset Size | 70,000 samples |
| Model Author | eleldar |
What is language-detection?
The language-detection model is a fine-tuned version of xlm-roberta-base specifically designed for language identification tasks. Built upon the XLM-RoBERTa architecture, it incorporates a classification head for accurate language detection across 20 different languages. The model achieves remarkable accuracy through careful fine-tuning on a comprehensive language identification dataset.
Implementation Details
The model utilizes a transformer-based architecture with a linear classification layer on top of the pooled output. Training was conducted using the Trainer API with carefully selected hyperparameters, including a learning rate of 2e-05 and mixed precision training. The model was trained for 2 epochs with a batch size of 64.
- Native AMP implementation for efficient training
- Adam optimizer with betas=(0.9,0.999)
- Linear learning rate scheduler
- Validation accuracy of 99.77%
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
- Outperforms baseline langid library (98.5% vs 99.6% accuracy)
- Excellent performance across all supported languages with most achieving F1-scores above 0.99
- Robust sequence classification for real-world applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional accuracy across a diverse range of languages, achieving 99.6% accuracy on the test set. It significantly outperforms traditional language detection tools while maintaining consistent performance across all supported languages.
Q: What are the recommended use cases?
The model is ideal for sequence classification tasks requiring language identification, particularly in multilingual content processing, content filtering, and automated language-based routing systems. It's especially effective for applications requiring high-accuracy language detection across the 20 supported languages.
