opus-mt-pl-fr
| Property | Value |
|---|---|
| Author | Helsinki-NLP |
| Architecture | Transformer-align |
| Source Language | Polish |
| Target Language | French |
| BLEU Score | 49.0 (Tatoeba) |
| Model URL | Hugging Face |
What is opus-mt-pl-fr?
opus-mt-pl-fr is a specialized neural machine translation model developed by Helsinki-NLP for translating Polish text to French. The model is based on the transformer-align architecture and has been trained on the OPUS dataset, demonstrating strong performance with a BLEU score of 49.0 on the Tatoeba test set.
Implementation Details
The model implements a transformer-align architecture with specific pre-processing steps including normalization and SentencePiece tokenization. It was trained on the OPUS dataset, a comprehensive collection of parallel texts.
- Pre-processing: Normalization + SentencePiece tokenization
- Architecture: Transformer-align
- Performance Metrics: 49.0 BLEU score and 0.659 chr-F score on Tatoeba test set
- Dataset: OPUS parallel corpus
Core Capabilities
- High-quality Polish to French translation
- Optimized for general-purpose translation tasks
- Strong performance on standardized test sets
- Suitable for production deployment
Frequently Asked Questions
Q: What makes this model unique?
This model specifically focuses on Polish to French translation, achieving impressive performance with a BLEU score of 49.0. Its transformer-align architecture and specialized pre-processing pipeline make it particularly effective for this language pair.
Q: What are the recommended use cases?
The model is well-suited for translating Polish text to French in various contexts, including document translation, content localization, and general-purpose translation tasks where high accuracy is required.
