sn-xlm-roberta-base-snli-mnli-anli-xnli
| Property | Value |
|---|---|
| Parameter Count | 278M |
| Model Type | Sentence Transformer |
| Supported Languages | 13 (ar, bg, de, el, en, es, fr, ru, th, tr, ur, vn, zh) |
| Training Datasets | SNLI, MNLI, ANLI, XNLI |
What is sn-xlm-roberta-base-snli-mnli-anli-xnli?
This is a powerful multilingual sentence transformer model based on XLM-RoBERTa architecture, specifically designed for zero-shot and few-shot text classification tasks. It maps sentences and paragraphs to 768-dimensional dense vector space, enabling sophisticated semantic analysis across 13 different languages.
Implementation Details
Built on the xlm-roberta-base architecture, this model has been extensively trained on four major natural language inference datasets: SNLI, MNLI, ANLI, and XNLI. It employs a Siamese network architecture for generating sentence embeddings and can be easily implemented using either the sentence-transformers library or HuggingFace Transformers.
- Generates 768-dimensional sentence embeddings
- Supports both sentence-transformers and HuggingFace implementations
- Utilizes mean pooling for embedding generation
- Optimized for cross-lingual applications
Core Capabilities
- Zero-shot and few-shot text classification
- Multilingual sentence similarity computation
- Cross-lingual feature extraction
- Semantic search and comparison
- Natural language inference tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's strength lies in its multilingual capabilities combined with extensive training on multiple NLI datasets, making it particularly effective for cross-lingual applications and zero-shot classification tasks.
Q: What are the recommended use cases?
This model excels in multilingual sentence similarity tasks, semantic search, text classification, and natural language inference applications. It's particularly useful when working with multiple languages or when zero-shot classification capabilities are needed.
