xlm-roberta-base-language-detection-onnx
| Property | Value |
|---|---|
| License | MIT |
| Paper | View Paper |
| Format | ONNX |
| Supported Languages | 21 |
What is xlm-roberta-base-language-detection-onnx?
This model is an ONNX-optimized version of the XLM-RoBERTa base model, specifically fine-tuned for language detection across 21 different languages. It leverages the powerful cross-lingual capabilities of the original XLM-RoBERTa architecture while providing enhanced performance through ONNX optimization.
Implementation Details
The model is implemented as a sequence classification task using a transformer architecture with a classification head. It's built on top of the xlm-roberta-base model and has been converted to ONNX format using the Hugging Face Optimum library for improved inference performance.
- Built on XLM-RoBERTa base architecture
- Optimized using ONNX format for efficient inference
- Includes a classification head for language detection
- Supports integration with Hugging Face Optimum library
Core Capabilities
- Detects 21 languages including Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Italian, Japanese, Dutch, Polish, Portuguese, Russian, Swahili, Thai, Turkish, Urdu, Vietnamese, and Chinese
- Provides high-accuracy language identification
- Offers efficient inference through ONNX optimization
- Supports batch processing of text sequences
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its ONNX optimization of the popular XLM-RoBERTa architecture, making it more efficient for production deployment while maintaining high accuracy in language detection across a wide range of languages.
Q: What are the recommended use cases?
The model is ideal for applications requiring language identification in multilingual contexts, such as content filtering, document classification, and automated language routing in NLP pipelines. It's particularly useful when processing content in any of the 21 supported languages.
