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 tokenizer.json from ebelenwaf/canbert: direct link, hf CLI and curl.
- Browser
- Download file 2.03 MB
-
https://huggingface.co/ebelenwaf/canbert/resolve/main/tokenizer.json
- Command line
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hf download hf://ebelenwaf/canbert/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/ebelenwaf/canbert/resolve/main/tokenizer.json
2.03 MB
File too large to display, you can check the raw version instead.