opus-mt-da-en
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Architecture | Transformer-align |
| Task | Danish to English Translation |
| BLEU Score | 63.6 (Tatoeba) |
What is opus-mt-da-en?
opus-mt-da-en is a specialized machine translation model developed by Helsinki-NLP for translating Danish text to English. Built on the transformer architecture, it demonstrates impressive performance with a BLEU score of 63.6 on the Tatoeba test set. The model has gained significant traction with over 62,000 downloads from the community.
Implementation Details
The model utilizes a transformer-align architecture and is trained on the OPUS dataset. It implements normalization and SentencePiece pre-processing techniques to enhance translation quality. The model supports both PyTorch and TensorFlow frameworks, making it versatile for different implementation environments.
- Pre-processing: Normalization + SentencePiece
- Framework compatibility: PyTorch and TensorFlow
- Training dataset: OPUS
Core Capabilities
- High-quality Danish to English translation
- Achieves 0.769 chr-F score on benchmark tests
- Supports inference endpoints for production deployment
- Compatible with popular deep learning frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its high BLEU score of 63.6 on the Tatoeba dataset, indicating exceptional translation quality for Danish to English conversion. It uses advanced pre-processing techniques and supports multiple deep learning frameworks.
Q: What are the recommended use cases?
This model is ideal for applications requiring Danish to English translation, such as content localization, document translation, and multilingual NLP applications. Its high performance makes it suitable for both research and production environments.
