opus-mt-en-jap
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch, TensorFlow |
| Task | English to Japanese Translation |
| BLEU Score | 42.1 (bible-uedin dataset) |
What is opus-mt-en-jap?
opus-mt-en-jap is a specialized machine translation model developed by Helsinki-NLP for translating English text to Japanese. Built on the transformer-align architecture, this model demonstrates exceptional performance with a BLEU score of 42.1 on the bible-uedin dataset, showcasing its capability in handling complex translations between these linguistically diverse languages.
Implementation Details
The model implements a transformer-align architecture and utilizes advanced pre-processing techniques including normalization and SentencePiece tokenization. It's trained on the OPUS dataset, making it particularly robust for various translation tasks.
- Pre-processing: Normalization + SentencePiece tokenization
- Architecture: Transformer-align
- Training Dataset: OPUS
- Evaluation Metric: BLEU score of 42.1 and chr-F score of 0.960
Core Capabilities
- High-quality English to Japanese text translation
- Supports both PyTorch and TensorFlow frameworks
- Optimized for production deployment with inference endpoints
- Robust performance on religious texts (demonstrated by bible-uedin benchmark)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its impressive BLEU score of 42.1 on religious texts and its versatile framework support, making it suitable for both research and production environments.
Q: What are the recommended use cases?
The model is particularly well-suited for English to Japanese translation tasks, especially in contexts involving formal or religious text translation. It can be deployed in both PyTorch and TensorFlow environments, making it versatile for various implementation scenarios.
