opus-mt-tc-big-en-el
| Property | Value |
|---|---|
| Model Type | Neural Machine Translation |
| Architecture | Transformer-big |
| Release Date | 2022-03-13 |
| Source Language | English |
| Target Language | Modern Greek |
| Best BLEU Score | 55.4 (Tatoeba test set) |
What is opus-mt-tc-big-en-el?
opus-mt-tc-big-en-el is a sophisticated neural machine translation model developed by Helsinki-NLP as part of the OPUS-MT project. This model specializes in translating from English to Modern Greek, leveraging the powerful transformer-big architecture and trained on the comprehensive OPUS dataset. The model implements SentencePiece tokenization with 32k vocabulary size for both source and target languages.
Implementation Details
The model is built using Marian NMT framework and later converted to PyTorch using the Hugging Face transformers library. It utilizes the transformer-big architecture, which is known for its superior performance in translation tasks. The model has been trained on the opusTCv20210807+bt dataset, incorporating both parallel corpora and back-translation techniques.
- SentencePiece tokenization with 32k vocabulary
- Transformer-big architecture implementation
- Achieves 73.66% chrF score on Tatoeba test set
- Compatible with Hugging Face transformers pipeline
Core Capabilities
- High-quality English to Modern Greek translation
- Handles complex sentence structures and idiomatic expressions
- Excellent performance on benchmark datasets (55.4 BLEU on Tatoeba)
- Seamless integration with popular ML frameworks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional performance on Greek translation, achieving a remarkable 55.4 BLEU score on the Tatoeba test set. It's part of the larger OPUS-MT initiative to democratize machine translation and uses advanced transformer-big architecture with comprehensive training data.
Q: What are the recommended use cases?
The model is ideal for professional translation systems, content localization, and applications requiring high-quality English to Modern Greek translation. It's particularly effective for general domain content and can be integrated into both research and production environments using the Hugging Face transformers library.
