opus-mt-ja-hu: Japanese to Hungarian Neural Machine Translation
| Property | Value |
|---|---|
| Model Architecture | transformer-align |
| Training Data | OPUS Dataset |
| BLEU Score | 12.2 |
| chrF Score | 0.364 |
| Release Date | 2020-06-17 |
| Source Languages | Japanese (multiple scripts) |
| Target Language | Hungarian |
What is opus-mt-ja-hu?
opus-mt-ja-hu is a specialized neural machine translation model developed by Helsinki-NLP for translating Japanese text to Hungarian. The model was trained using the transformer-align architecture and supports various Japanese writing systems including Hiragana, Katakana, Kanji, and other scripts.
Implementation Details
The model employs normalization and SentencePiece tokenization with 32k vocabulary size for both source and target languages. It was trained on the OPUS parallel corpus and demonstrates moderate performance with a BLEU score of 12.2 and a chrF score of 0.364 on the Tatoeba test set.
- Preprocessing: Normalization + SentencePiece (spm32k,spm32k)
- Architecture: transformer-align with attention mechanism
- Support for multiple Japanese scripts (Bopomofo, Hanzi, Hiragana, Katakana, Yi)
- Single target language: Hungarian
Core Capabilities
- Direct Japanese to Hungarian translation
- Handling of multiple Japanese writing systems
- Normalized text processing
- Subword tokenization for better handling of rare words
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically designed for the relatively rare language pair of Japanese-Hungarian translation, supporting multiple Japanese writing systems while targeting Hungarian exclusively. Its transformer-align architecture and comprehensive preprocessing make it suitable for production use despite the challenging language pair.
Q: What are the recommended use cases?
The model is best suited for general-purpose Japanese to Hungarian translation tasks. With moderate performance metrics, it's recommended for applications where general meaning transfer is sufficient, though human review may be needed for critical translations.
