OPUS-MT English-Hindi Translation Model
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Developer | Helsinki-NLP |
| BLEU Score | 16.1 |
| chrF Score | 0.447 |
| Architecture | transformer-align |
What is opus-mt-en-hi?
The opus-mt-en-hi is a specialized machine translation model developed by Helsinki-NLP for translating English text to Hindi. It utilizes the transformer-align architecture and has been trained using the OPUS parallel corpus. With over 29,500 downloads, this model demonstrates significant practical adoption in the translation community.
Implementation Details
The model employs a transformer-align architecture with SentencePiece tokenization (spm32k,spm32k) for both source and target languages. Pre-processing includes normalization steps to ensure optimal translation quality. The model was trained on June 17, 2020, and has shown consistent performance across various test sets.
- Implements SentencePiece tokenization with 32k vocabulary
- Includes comprehensive normalization preprocessing
- Supports both PyTorch and TensorFlow frameworks
- Achieves 16.1 BLEU score on Tatoeba test set
Core Capabilities
- English to Hindi text translation
- Handles various text formats and lengths
- Performance benchmarks: newsdev2014 (6.9 BLEU), newstest2014 (9.9 BLEU), Tatoeba-test (16.1 BLEU)
- Suitable for both research and production environments
Frequently Asked Questions
Q: What makes this model unique?
The model's transformer-align architecture combined with specialized SentencePiece tokenization makes it particularly effective for English-Hindi translation, achieving competitive BLEU scores on standard benchmarks.
Q: What are the recommended use cases?
This model is ideal for English to Hindi translation tasks in both academic and production environments, particularly where normalized and consistent translations are required. It's especially effective for Tatoeba-style conversational content, where it achieves its highest BLEU score.
