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")# 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
add model
Browse files- config.json +2 -0
- pytorch_model.bin +1 -1
config.json
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{
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"activation_dropout": 0.1,
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"activation_function": "gelu",
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"add_bias_logits": false,
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"num_beams": 6
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}
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},
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"transformers_version": "4.21.3",
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"use_cache": true,
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"vocab_size": 12660
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{
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"_name_or_path": "/tmp/tmpaq0rfnby",
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"activation_dropout": 0.1,
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"activation_function": "gelu",
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"add_bias_logits": false,
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"num_beams": 6
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}
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},
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"torch_dtype": "float32",
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"transformers_version": "4.21.3",
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"use_cache": true,
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"vocab_size": 12660
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 439094139
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version https://git-lfs.github.com/spec/v1
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oid sha256:303e92093f56e404a313f11a58c6f6be38dbbfab4ce2cef6218a33bb2dff4bd0
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size 439094139
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