XLM-Roberta-Large-Vit-B-16Plus
| Property | Value |
|---|---|
| Author | M-CLIP |
| Downloads | 65,202 |
| Languages Supported | 48 |
| Framework | PyTorch, TensorFlow |
What is XLM-Roberta-Large-Vit-B-16Plus?
XLM-Roberta-Large-Vit-B-16Plus is a sophisticated multilingual CLIP model that extends OpenAI's CLIP architecture to support 48 different languages. It represents a significant advancement in multilingual text-image understanding, achieving state-of-the-art performance across various languages.
Implementation Details
The model consists of two main components: a multilingual text encoder based on XLM-RoBERTa Large architecture and a vision encoder using ViT-B-16Plus. It leverages the powerful CLIP training methodology while expanding its capabilities to multiple languages.
- Achieves 95.0% R@10 score for English, significantly outperforming previous models
- Supports comprehensive language coverage including Arabic, Chinese, Russian, and many more
- Implements efficient text-to-image retrieval capabilities
Core Capabilities
- Multilingual text encoding for 48 languages
- High-performance image-text matching
- Superior R@10 scores across all supported languages
- Seamless integration with both PyTorch and TensorFlow frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional multilingual capabilities and state-of-the-art performance metrics, particularly achieving the highest R@10 scores across multiple languages (95.0% for English, 93.0% for German, etc.). It's particularly notable for maintaining consistent high performance across all 48 supported languages.
Q: What are the recommended use cases?
The model is ideal for multilingual text-image retrieval tasks, cross-lingual image search, and building multilingual visual-semantic applications. It's particularly useful for applications requiring robust performance across multiple languages while maintaining high accuracy in image-text matching.
