Instructions to use Ayaka/bart-base-cantonese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ayaka/bart-base-cantonese with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Ayaka/bart-base-cantonese")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ayaka/bart-base-cantonese") model = AutoModel.from_pretrained("Ayaka/bart-base-cantonese", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ayaka/bart-base-cantonese: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/Ayaka/bart-base-cantonese/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ayaka/bart-base-cantonese/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ayaka/bart-base-cantonese/resolve/main/pytorch_model.bin
439 MB
- Xet hash:
- 473ef2dc5e612065c0a677de53935c9d4e938fe279163e298f02efea5b468c29
- Size of remote file:
- 439 MB
- SHA256:
- 303e92093f56e404a313f11a58c6f6be38dbbfab4ce2cef6218a33bb2dff4bd0
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