XLM-RoBERTa Base SNLI-MNLI-ANLI-XNLI
| Property | Value |
|---|---|
| Author | Symanto |
| Base Architecture | XLM-RoBERTa Base |
| Task | Natural Language Inference |
| Model Hub | Hugging Face |
What is xlm-roberta-base-snli-mnli-anli-xnli?
This is a sophisticated cross-lingual Natural Language Inference (NLI) model that builds upon the XLM-RoBERTa base architecture. It's specifically designed for zero-shot and few-shot text classification tasks across multiple languages, having been trained on a comprehensive dataset combination of SNLI, MNLI, ANLI, and XNLI.
Implementation Details
The model leverages the powerful XLM-RoBERTa base architecture and has been fine-tuned for natural language inference tasks. It accepts input pairs of text and hypothesis, processing them through a cross-attention mechanism to determine their logical relationship.
- Built on XLM-RoBERTa base architecture
- Trained on multiple NLI datasets for robust performance
- Supports zero-shot and few-shot classification
- Multi-lingual capability demonstrated across English, German, Spanish, and other languages
Core Capabilities
- Cross-lingual natural language inference
- Zero-shot classification across different languages
- Sentence pair classification with probability outputs
- Support for multiple languages with consistent performance
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its ability to perform cross-lingual natural language inference tasks without requiring language-specific training. It can handle multiple languages and provides probability scores for inference relationships between text pairs.
Q: What are the recommended use cases?
The model is ideal for multilingual text classification, sentiment analysis, and natural language inference tasks. It's particularly useful when you need to classify text across different languages without explicit training data for each language.
