Instructions to use almanach/camembert-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use almanach/camembert-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="almanach/camembert-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("almanach/camembert-base") model = AutoModelForMaskedLM.from_pretrained("almanach/camembert-base") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63b043324dcc4e73937509e98070394cac11e91bc9d4efe2577ed3a4dfaf2b70
- Size of remote file:
- 445 MB
- SHA256:
- 54ca0c5f4daf6885f7b07df460624de6120fe5cf964f9b082a4874be6249f5f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.