Instructions to use Helsinki-NLP/opus-mt-mg-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-mg-en with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-mg-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-mg-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-mg-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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tags:
- translation
license: apache-2.0
---
### opus-mt-mg-en
* source languages: mg
* target languages: en
* OPUS readme: [mg-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mg-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-09.zip](https://object.pouta.csc.fi/OPUS-MT-models/mg-en/opus-2020-01-09.zip)
* test set translations: [opus-2020-01-09.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/mg-en/opus-2020-01-09.test.txt)
* test set scores: [opus-2020-01-09.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/mg-en/opus-2020-01-09.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| GlobalVoices.mg.en | 27.6 | 0.522 |
| Tatoeba.mg.en | 50.2 | 0.607 |
|