opus-mt-en-tl
| Property | Value |
|---|---|
| Developer | Helsinki-NLP |
| Model Type | Transformer-align |
| Languages | English to Tagalog |
| BLEU Score | 26.6 on Tatoeba |
| Model URL | Hugging Face |
What is opus-mt-en-tl?
opus-mt-en-tl is a specialized neural machine translation model designed to translate text from English to Tagalog (Filipino). Developed by Helsinki-NLP, this model utilizes the transformer-align architecture and has been trained on the OPUS dataset augmented with back-translation data.
Implementation Details
The model implements a transformer-align architecture with specific pre-processing steps including normalization and SentencePiece tokenization. It has been trained on the opus+bt dataset, demonstrating robust performance with a chr-F score of 0.577 on evaluation tests.
- Pre-processing pipeline includes normalization and SentencePiece tokenization
- Trained on OPUS dataset with back-translation enhancement
- Achieves 26.6 BLEU score on Tatoeba test set
- Uses transformer-align architecture for improved alignment in translations
Core Capabilities
- High-quality English to Tagalog translation
- Optimized for general-purpose translation tasks
- Suitable for both casual and professional translation needs
- Demonstrated effectiveness on standardized test sets
Frequently Asked Questions
Q: What makes this model unique?
This model specifically focuses on English to Tagalog translation, utilizing the transformer-align architecture and achieving competitive BLEU scores. Its training on the OPUS dataset plus back-translated data makes it particularly robust for this language pair.
Q: What are the recommended use cases?
The model is well-suited for general-purpose English to Tagalog translation tasks, including document translation, content localization, and automated translation systems where Tagalog output is required. The strong BLEU score of 26.6 indicates reliable performance for most common translation scenarios.
