xlm-roberta-large-xnli
| Property | Value |
|---|---|
| Parameter Count | 561M |
| License | MIT |
| Paper | Research Paper |
| Supported Languages | 16 |
| Downloads | 126,589 |
What is xlm-roberta-large-xnli?
XLM-RoBERTa-large-xnli is a powerful multilingual model fine-tuned for zero-shot text classification. Built upon the XLM-RoBERTa architecture, it has been specifically trained on a combination of NLI (Natural Language Inference) data across 15 different languages, making it exceptionally versatile for cross-lingual applications.
Implementation Details
The model leverages a robust architecture with 561M parameters and supports both PyTorch and TensorFlow frameworks. It implements zero-shot classification through a sophisticated NLI approach, allowing users to classify texts without requiring training data for specific categories.
- Pre-trained on 100 languages using XLM-RoBERTa architecture
- Fine-tuned on MNLI and XNLI datasets
- Supports seamless cross-lingual classification
- Compatible with the Hugging Face zero-shot classification pipeline
Core Capabilities
- Zero-shot text classification in 16 languages including English, French, Spanish, German, Russian, and more
- Cross-lingual label mapping (labels can be in different languages from the input text)
- Flexible hypothesis template customization
- High accuracy in multilingual classification tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to perform zero-shot classification across 16 languages while allowing mix-and-match of input text and label languages. Its specialized training on XNLI data makes it particularly effective for cross-lingual applications.
Q: What are the recommended use cases?
The model is ideal for multilingual text classification tasks, especially when working with languages beyond English. It's particularly useful for content categorization, sentiment analysis, and topic classification in international applications where training data might not be available for all languages.
