Translation
Transformers
PyTorch
TensorFlow
Seselwa Creole French
German
marian
text2text-generation
Instructions to use Helsinki-NLP/opus-mt-crs-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-crs-de 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-crs-de")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-crs-de") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-crs-de") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 151e9240d4043b377df9ab9d4c32dcc30c000fd22b1d75195ac9c5af7accd67e
- Size of remote file:
- 276 MB
- SHA256:
- 8baefb135a1a3dbaae4f3e38b7816e08953f294c6ed8a835722e25e9f68369be
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.