Instructions to use Jodsa/camembert_mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jodsa/camembert_mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Jodsa/camembert_mlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Jodsa/camembert_mlm") model = AutoModelForMaskedLM.from_pretrained("Jodsa/camembert_mlm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jodsa/camembert_mlm: direct link, hf CLI and curl.
- Browser
- Download file 448 MB
-
https://huggingface.co/Jodsa/camembert_mlm/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jodsa/camembert_mlm/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jodsa/camembert_mlm/resolve/main/pytorch_model.bin
448 MB
- Xet hash:
- 556834463673ab246b46d0d0ce5c47f0bc743359367f93eac752644be75d4a76
- Size of remote file:
- 448 MB
- SHA256:
- 352e4fd5688bbd84d4006b21591bbc68b9d313414d6d7a662f3d029f43513dea
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