Translation Model OPUS
| Property | Value |
|---|---|
| License | Apache-2.0 |
| Language Pair | English-Spanish |
| BLEU Score | 54.9 |
| chrF2 Score | 0.721 |
| Training Date | August 18, 2020 |
What is translation-model-opus?
Translation-model-opus is a state-of-the-art machine translation model specifically designed for English-Spanish translation tasks. Built using the transformer architecture and trained on the OPUS dataset, this model demonstrates excellent performance with a BLEU score of 54.9 on the Tatoeba test set.
Implementation Details
The model utilizes a transformer-based architecture with normalization and SentencePiece tokenization (spm32k,spm32k). It was trained using PyTorch and supports bidirectional translation between English and Spanish.
- Pre-processing: Normalization + SentencePiece tokenization
- Architecture: Transformer-based neural network
- Training Framework: PyTorch
- Evaluation Metrics: BLEU (54.9) and chrF2 (0.721)
Core Capabilities
- High-quality English to Spanish translation
- Robust performance across various test sets (news, general content)
- Consistent performance on news translation tasks (BLEU scores ranging from 29.7 to 39.0)
- Optimized for production deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance on the Tatoeba test set, achieving a BLEU score of 54.9 and chrF2 score of 0.721, making it particularly reliable for English-Spanish translation tasks. It has been extensively tested across various news translation benchmarks, demonstrating consistent performance.
Q: What are the recommended use cases?
The model is well-suited for: news translation, general content translation, and production environments requiring reliable English-Spanish translation capabilities. It performs particularly well on news content, as evidenced by its strong performance across multiple news test sets.
