Instructions to use PhilippW/distilbart-big5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhilippW/distilbart-big5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PhilippW/distilbart-big5")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("PhilippW/distilbart-big5") model = AutoModelForSequenceClassification.from_pretrained("PhilippW/distilbart-big5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from PhilippW/distilbart-big5: direct link, hf CLI and curl.
- Browser
- Download file 1.02 GB
-
https://huggingface.co/PhilippW/distilbart-big5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://PhilippW/distilbart-big5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/PhilippW/distilbart-big5/resolve/main/pytorch_model.bin
1.02 GB
- Xet hash:
- 86f8f52bec329b408d04d0be8e8a197486c1b941dbe1f4724c2ff7ed22021c8b
- Size of remote file:
- 1.02 GB
- SHA256:
- 675e78ec9a8a2eacde6650d909049f8e72933dce23d8d0bfa5bbf24c068bb9f1
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