Instructions to use indiejoseph/bert-base-cantonese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use indiejoseph/bert-base-cantonese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="indiejoseph/bert-base-cantonese")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("indiejoseph/bert-base-cantonese") model = AutoModelForMaskedLM.from_pretrained("indiejoseph/bert-base-cantonese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from indiejoseph/bert-base-cantonese: direct link, hf CLI and curl.
- Browser
- Download file 411 MB
-
https://huggingface.co/indiejoseph/bert-base-cantonese/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://indiejoseph/bert-base-cantonese/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/indiejoseph/bert-base-cantonese/resolve/main/pytorch_model.bin
411 MB
- Xet hash:
- 878b08e23eeef28863b280bccb5d2474a16cd4acd583fee42ede16249ee9a689
- Size of remote file:
- 411 MB
- SHA256:
- 85577ee78a1d1e650d745ec20ab3ab39fd79b11d77d6e33fecde629de444f4ea
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