Instructions to use Helsinki-NLP/opus-mt-fr-bem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-fr-bem 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-bem")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-bem") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-fr-bem", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68ede0719ec0e27b188ce1fa02165c0629772663ac0fbb46316bc7a91a9a8779
- Size of remote file:
- 304 MB
- SHA256:
- c31cd656f72d268957b7b7a4d94d64b7d61ad6099bb6b08557758839b7d63b22
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.