opus-mt-sv-en
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Framework | PyTorch, TensorFlow |
| Task | Translation (Swedish to English) |
| BLEU Score | 64.5 on Tatoeba |
What is opus-mt-sv-en?
opus-mt-sv-en is a specialized machine translation model developed by Helsinki-NLP for translating Swedish text to English. Built on the transformer-align architecture, it has demonstrated impressive performance with a BLEU score of 64.5 and a chr-F score of 0.763 on the Tatoeba test set.
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 optimize translation quality. The model supports both PyTorch and TensorFlow frameworks, making it versatile for different implementation environments.
- Pre-processing pipeline includes normalization and SentencePiece tokenization
- Trained on comprehensive OPUS dataset
- Supports multiple deep learning frameworks
- Implements transformer-align architecture
Core Capabilities
- High-quality Swedish to English translation
- Strong performance on general text translation tasks
- Efficient processing through modern transformer architecture
- Proven accuracy with 64.5 BLEU score on benchmark tests
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its high BLEU score of 64.5 on the Tatoeba test set, indicating exceptional translation quality for Swedish to English translation tasks. It benefits from the robust transformer-align architecture and comprehensive OPUS dataset training.
Q: What are the recommended use cases?
This model is ideal for applications requiring Swedish to English translation, including content localization, document translation, and automated translation systems. It's particularly suitable for production environments due to its support for both PyTorch and TensorFlow frameworks.
