MADLAD-400-3B-MT
| Property | Value |
|---|---|
| Parameter Count | 2.94B |
| Model Type | Text-to-Text Translation |
| Architecture | T5-based |
| License | Apache 2.0 |
| Research Paper | arXiv:2309.04662 |
What is madlad400-3b-mt?
MADLAD-400-3B-MT is a groundbreaking multilingual machine translation model that represents a significant advancement in natural language processing. Trained on 1 trillion tokens covering over 450 languages, it uses the T5 architecture to deliver high-quality translations across an unprecedented number of language pairs. The model demonstrates competitive performance against much larger models, making it an efficient choice for multilingual applications.
Implementation Details
The model is implemented using the transformers library and utilizes a T5-based architecture with shared parameters across language pairs. It employs a Sentence Piece Model with 256k tokens shared between encoder and decoder. Translation tasks are handled by prepending a special language token (e.g., "<2en>" for English) to source sentences.
- Transformer-based architecture with 32 layers
- Supports F32 tensor operations
- Implements text-to-text generation pipeline
- Compatible with both CPU and GPU deployment
Core Capabilities
- Direct translation between 419 supported languages
- High-quality performance on both high and low-resource languages
- Efficient parameter usage compared to larger models
- Support for domain-general translation tasks
- Integration with popular ML frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle over 400 languages while maintaining competitive performance with just 2.94B parameters makes it unique. 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 best suited for research applications in multilingual NLP tasks, particularly for general domain translations. However, it's important to note that it's not specifically optimized for domain-specific translations or production environments without further evaluation.
