opus-mt-hu-en
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Framework | Marian |
| Task | Translation (Hungarian to English) |
| BLEU Score | 52.9 (Tatoeba) |
What is opus-mt-hu-en?
opus-mt-hu-en is a specialized neural machine translation model developed by Helsinki-NLP for translating Hungarian text to English. Built on the transformer-align architecture, this model demonstrates impressive performance with a BLEU score of 52.9 on the Tatoeba test set, making it a reliable choice for Hungarian-English translation tasks.
Implementation Details
The model utilizes the transformer-align architecture and is implemented using the Marian framework. Pre-processing includes normalization and SentencePiece tokenization, ensuring optimal handling of Hungarian text characteristics. The model was trained on the OPUS dataset, a comprehensive collection of parallel texts.
- Transformer-align architecture for enhanced translation quality
- SentencePiece tokenization for efficient text processing
- Normalization pipeline for consistent input handling
- Trained on the OPUS parallel corpus
Core Capabilities
- High-quality Hungarian to English translation
- Achieves 0.683 chr-F score on benchmark tests
- Supports inference endpoints for deployment
- Compatible with PyTorch and TensorFlow frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Hungarian-English translation and impressive BLEU score of 52.9 on the Tatoeba dataset, making it particularly effective for this language pair. The implementation of transformer-align architecture with SentencePiece tokenization ensures high-quality translations.
Q: What are the recommended use cases?
The model is ideal for applications requiring Hungarian to English translation, such as content localization, document translation, and automated translation services. Its high BLEU score makes it suitable for both professional and academic applications where accuracy is crucial.
