InfoXLM-Large
| Property | Value |
|---|---|
| Author | Microsoft |
| Downloads | 2,850,270 |
| Paper | View Paper |
| Tags | Fill-Mask, Transformers, PyTorch, XLM-RoBERTa |
What is infoxlm-large?
InfoXLM-Large is a sophisticated cross-lingual language model developed by Microsoft, introduced in NAACL 2021. It implements an innovative information-theoretic framework for pre-training, specifically designed to enhance cross-lingual understanding and transfer learning capabilities.
Implementation Details
The model is built upon the XLM-RoBERTa architecture and utilizes advanced information-theoretic principles for cross-lingual pre-training. It employs specialized training objectives to maximize mutual information across different languages while maintaining semantic coherence.
- Implements fill-mask functionality for multilingual text processing
- Built using PyTorch framework for efficient computation
- Supports inference endpoints for practical deployment
- Incorporates advanced cross-lingual training techniques
Core Capabilities
- Cross-lingual understanding and representation
- Masked language modeling across multiple languages
- Transfer learning for low-resource languages
- Efficient multilingual text processing
Frequently Asked Questions
Q: What makes this model unique?
InfoXLM stands out due to its information-theoretic framework that optimizes cross-lingual representation learning, making it particularly effective for multilingual tasks and transfer learning scenarios.
Q: What are the recommended use cases?
The model is ideal for cross-lingual tasks such as translation, multilingual text classification, cross-lingual question answering, and zero-shot transfer learning across languages.
