MADLAD-400-10B-MT
| Property | Value |
|---|---|
| Parameter Count | 10.7B |
| License | Apache 2.0 |
| Architecture | T5-based |
| Paper | Research Paper |
| Languages | 419+ |
What is MADLAD-400-10B-MT?
MADLAD-400-10B-MT is a state-of-the-art multilingual machine translation model that represents a significant advancement in language AI. Trained on 250 billion tokens covering over 450 languages, it uses a T5 architecture to deliver high-quality translations across an unprecedented number of language pairs. The model was developed by Google Research and demonstrates competitive performance against larger models while maintaining efficiency.
Implementation Details
The model employs a 32-layer architecture with shared parameters across language pairs. It utilizes a Sentence Piece Model with 256k tokens shared between encoder and decoder components. Input processing includes a special language token (e.g., <2en> for English) to indicate the target language for translation.
- Advanced parameter sharing architecture across all language pairs
- Comprehensive tokenizer with 256k vocabulary size
- Efficient processing with specialized language tokens
- Support for both CPU and GPU deployment
Core Capabilities
- Direct translation between 419+ languages
- High-quality output comparable to larger models
- Efficient processing of multilingual content
- Support for low-resource languages
- Flexible deployment options with different frameworks
Frequently Asked Questions
Q: What makes this model unique?
MADLAD-400-10B-MT stands out for its exceptional coverage of over 419 languages while maintaining high translation quality. It's particularly notable for supporting many low-resource languages that are typically underrepresented in machine translation systems.
Q: What are the recommended use cases?
The model is primarily designed for research applications in machine translation and multilingual NLP tasks. It's particularly valuable for academic research involving low-resource languages and multilingual applications, though it's not specifically optimized for production environments.
