Instructions to use ebelenwaf/canbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ebelenwaf/canbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ebelenwaf/canbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ebelenwaf/canbert") model = AutoModelForMaskedLM.from_pretrained("ebelenwaf/canbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ebelenwaf/canbert: direct link, hf CLI and curl.
- Browser
- Download file 334 MB
-
https://huggingface.co/ebelenwaf/canbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ebelenwaf/canbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ebelenwaf/canbert/resolve/main/pytorch_model.bin
334 MB
- Xet hash:
- 19d21c36cee7add943976aecd7463ee9137d90525f918831ce80b3fbb6cbd28a
- Size of remote file:
- 334 MB
- SHA256:
- a9d3e341413709742354dcbb066f2d5ae3d11e64dae8d88389e26898c95c65a3
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