opus-mt-en-es
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Developer | Helsinki-NLP |
| Architecture | Transformer (Marian) |
| BLEU Score | 54.9 (Tatoeba test) |
| Downloads | 181,410 |
What is opus-mt-en-es?
opus-mt-en-es is a specialized neural machine translation model designed to translate text from English to Spanish. Developed by Helsinki-NLP, this transformer-based model has demonstrated impressive performance with a BLEU score of 54.9 on the Tatoeba test set, making it highly reliable for English-Spanish translation tasks.
Implementation Details
The model utilizes a transformer architecture implemented using the Marian framework. It employs normalization and SentencePiece tokenization with 32k vocabulary size for both source and target languages. The model has been extensively trained and evaluated on various news and general-domain test sets.
- Pre-processing: Normalization + SentencePiece (spm32k,spm32k)
- Architecture: Transformer-based neural network
- Training Date: August 18, 2020
- Evaluation Metrics: Both BLEU and chrF scoring systems
Core Capabilities
- High-quality English to Spanish translation
- Consistent performance across various domains (news, general text)
- Strong performance on news translation (35-39 BLEU on news test sets)
- Excellent results on conversational text (54.9 BLEU on Tatoeba)
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance on both formal and informal translation tasks, with particularly strong results on the Tatoeba test set (54.9 BLEU). It has been extensively tested across various news domains, showing consistent performance levels above 30 BLEU points.
Q: What are the recommended use cases?
The model is well-suited for general-purpose English to Spanish translation, particularly effective for news content, documentation, and conversational text. It's ideal for applications requiring reliable automated translation with high accuracy.
