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 flax_model.msgpack 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/flax_model.msgpack
- Command line
-
hf download hf://Ayaka/bart-base-cantonese/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Ayaka/bart-base-cantonese/resolve/main/flax_model.msgpack
439 MB
- Xet hash:
- 3ae6040a7e676b1d9d092f1e41a4f327d76bff50e1b310cdf6ea638ea863c060
- Size of remote file:
- 439 MB
- SHA256:
- 88ccb433289f83a442b26e0249c2c45fb82d1fa7fe24c092dc176005adf79e5a
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