Instructions to use UIC-Liu-Lab/CPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UIC-Liu-Lab/CPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="UIC-Liu-Lab/CPT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("UIC-Liu-Lab/CPT", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("UIC-Liu-Lab/CPT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from UIC-Liu-Lab/CPT: direct link, hf CLI and curl.
- Browser
- Download file 594 MB
-
https://huggingface.co/UIC-Liu-Lab/CPT/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://UIC-Liu-Lab/CPT/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/UIC-Liu-Lab/CPT/resolve/main/pytorch_model.bin
594 MB
- Xet hash:
- 6e720cf835132910406ddbbad01243a878686cc13f0d83e25780796bb8e3a096
- Size of remote file:
- 594 MB
- SHA256:
- e17d312f7eeafb919e61b2e50fa0216dfe446599d1991c22e983aee7e9147c9c
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