Instructions to use WindyTranslate/translate-fr-bem with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WindyTranslate/translate-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="WindyTranslate/translate-fr-bem")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("WindyTranslate/translate-fr-bem") model = AutoModelForSeq2SeqLM.from_pretrained("WindyTranslate/translate-fr-bem", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download source.spm from WindyTranslate/translate-fr-bem: direct link, hf CLI and curl.
- Browser
- Download file 844 kB
-
https://huggingface.co/WindyTranslate/translate-fr-bem/resolve/main/source.spm
- Command line
-
hf download hf://WindyTranslate/translate-fr-bem/source.spm
-
curl -L -o source.spm https://huggingface.co/WindyTranslate/translate-fr-bem/resolve/main/source.spm
844 kB
- Xet hash:
- 149f1579332ee1905e8fbb3371bc78019e09383effad970e7660f439772f85ab
- Size of remote file:
- 844 kB
- SHA256:
- 2d270b70ed072afb9af9355a81f55a0e3e5be3f3ce2929c5b0d6ce0f0ad59b61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.