opus-mt-tc-big-en-hu
| Property | Value |
|---|---|
| Model Type | Neural Machine Translation |
| Architecture | Transformer-big |
| Languages | English to Hungarian |
| Release Date | 2022-02-25 |
| Training Data | OPUS TC v2021.08.07 |
| Tokenization | SentencePiece (spm32k) |
What is opus-mt-tc-big-en-hu?
opus-mt-tc-big-en-hu is a state-of-the-art neural machine translation model specifically designed for translating from English to Hungarian. Developed by Helsinki-NLP as part of the OPUS-MT project, this model demonstrates impressive performance with a BLEU score of 38.7 on the Tatoeba test set and 29.6 on the FLORES101 benchmark.
Implementation Details
The model is built using the Marian NMT framework and converted to PyTorch using the Hugging Face Transformers library. It employs the transformer-big architecture with SentencePiece tokenization (32k vocabulary) for both source and target languages.
- Trained on OPUS and Tatoeba Challenge datasets
- Implements the transformer-big architecture for enhanced performance
- Uses SentencePiece tokenization for efficient processing
- Supports batch translation and integration with Hugging Face pipelines
Core Capabilities
- High-quality English to Hungarian translation
- Strong performance on multiple benchmark datasets
- Efficient processing of both formal and informal text
- Easy integration with Python applications using Transformers library
- Supports batch translation for multiple sentences
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its impressive performance on Hungarian translation, with particularly strong results on the Tatoeba test set (BLEU 38.7). It's part of the larger OPUS-MT initiative to make high-quality translation accessible for many language pairs.
Q: What are the recommended use cases?
The model is ideal for applications requiring English to Hungarian translation, including content localization, document translation, and integration into larger language processing pipelines. It's particularly effective for general-domain text translation.
