opus-mt-es-fr
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Framework | PyTorch/TensorFlow |
| Task | Spanish to French Translation |
| Architecture | Transformer-align |
What is opus-mt-es-fr?
opus-mt-es-fr is a neural machine translation model developed by Helsinki-NLP, specifically designed for translating Spanish text to French. Built on the transformer architecture, this model has demonstrated impressive performance across various benchmark datasets, including achieving a remarkable BLEU score of 58.4 on the Tatoeba dataset.
Implementation Details
The model employs a transformer-align architecture and utilizes advanced pre-processing techniques including normalization and SentencePiece tokenization. It was trained on the OPUS dataset, providing robust translation capabilities for Spanish to French language pairs.
- Pre-processing: Normalization + SentencePiece tokenization
- Training dataset: OPUS corpus
- Architecture: Transformer-align with attention mechanism
- Evaluation metrics: BLEU and chr-F scores
Core Capabilities
- High-quality Spanish to French translation with BLEU scores ranging from 32.0 to 58.4
- Consistent performance across various news test sets (2008-2013)
- Excellent performance on conversational text (Tatoeba dataset)
- Support for both PyTorch and TensorFlow frameworks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its consistent performance across different types of content, particularly excelling in news translation with BLEU scores consistently above 32.0. Its exceptional performance on the Tatoeba dataset (58.4 BLEU) makes it particularly suitable for conversational text translation.
Q: What are the recommended use cases?
This model is well-suited for translating news content, general text, and conversational Spanish to French translation tasks. It's particularly effective for content similar to news articles and everyday conversational text, as evidenced by its benchmark scores.
