Instructions to use Helsinki-NLP/opus-mt-fr-mh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-fr-mh 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-fr-mh")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-mh") model = AutoModelForMultimodalLM.from_pretrained("Helsinki-NLP/opus-mt-fr-mh") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c54d55f088409e97b8865d0c3dd8209e341324cf71b00091468126499106287e
- Size of remote file:
- 288 MB
- SHA256:
- 40f2706a77cabb79ea077283d3b1219594fb8e8253eb39efae2031eda4bf91f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.