MBart
MBart is a sequence-to-sequence denoising auto-encoder pretrained model for neural machine translation.
What is MBart?
MBart is a state-of-the-art sequence-to-sequence denoising auto-encoder pretrained model designed for neural machine translation tasks. It is pretrained on large-scale monolingual corpora in many languages using the BART (Bidirectional and Auto-Regressive Transformers) model architecture.
MBart learns to generate high-quality translations by denoising corrupted versions of sentences in multiple languages. The model is trained on a large amount of monolingual data, enabling it to capture cross-lingual patterns and transfer knowledge between languages. By leveraging transfer learning, MBart significantly improves translation performance, especially for low-resource languages.
MBart is based on the Transformer model family, which has been widely used in natural language processing tasks. It has inspired the development of various models for different domains and applications, such as protein structure prediction, image captioning, and time series forecasting.
Key Features and Benefits
- Sequence-to-sequence denoising auto-encoder.
- Pretrained on large-scale monolingual corpora.
- Supports multiple languages.
- Transfer learning for improved translation performance.
Use Cases
- Neural machine translation.
- Cross-lingual transfer learning.
- Improving translation quality.
- Low-resource language translation.
