opus-mt-id-en
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch, TensorFlow |
| Task | Translation (Indonesian to English) |
| BLEU Score | 47.7 (Tatoeba) |
What is opus-mt-id-en?
opus-mt-id-en is a specialized neural machine translation model developed by Helsinki-NLP for translating Indonesian text to English. Built on the Marian framework, it utilizes a transformer-align architecture and has demonstrated strong performance with a BLEU score of 47.7 on the Tatoeba test set.
Implementation Details
The model employs a sophisticated pre-processing pipeline that includes normalization and SentencePiece tokenization. It's trained on the OPUS dataset, providing robust translation capabilities for Indonesian to English language pairs.
- Transformer-align architecture optimized for translation tasks
- Comprehensive preprocessing with SentencePiece tokenization
- Trained on the extensive OPUS dataset
- Supports both PyTorch and TensorFlow frameworks
Core Capabilities
- High-quality Indonesian to English translation
- Achieves 0.647 chr-F score on benchmark tests
- Suitable for production deployment via Inference Endpoints
- Handles various Indonesian text inputs effectively
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Indonesian-English translation, achieving impressive benchmark scores and utilizing a transformer-align architecture with careful preprocessing steps.
Q: What are the recommended use cases?
The model is ideal for applications requiring Indonesian to English translation, including content localization, document translation, and automated translation services. With its strong BLEU score, it's particularly suitable for production environments requiring accurate translations.
