Translation
Transformers
PyTorch
TensorFlow
English
Seselwa Creole French
marian
text2text-generation
Instructions to use Helsinki-NLP/opus-mt-en-crs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-en-crs 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-en-crs")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-crs") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-crs") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25c064676d574869d3d5d6e0cb6ace7f1559013669de2fcd358e6c61fb307f68
- Size of remote file:
- 268 MB
- SHA256:
- 16743ee563b9c9dd821eecdca15749e1d7cd7cea160936e7d5154868870acbf8
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