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 training_args.bin from ebelenwaf/canbert: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/ebelenwaf/canbert/resolve/main/training_args.bin
- Command line
-
hf download hf://ebelenwaf/canbert/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ebelenwaf/canbert/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- 98a3b34ec19c79fb9844dfa02e81b987c0c60d38267f0811582937cd7b9dd04d
- Size of remote file:
- 3.25 kB
- SHA256:
- 3d7945c0f99c3a54cc561ef8e0a719b516ca2cb30af3aab78fe76fc76f7b2a57
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