Instructions to use tomlobato/prop_class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tomlobato/prop_class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tomlobato/prop_class")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("tomlobato/prop_class") model = AutoModelForSequenceClassification.from_pretrained("tomlobato/prop_class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tomlobato/prop_class: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/tomlobato/prop_class/resolve/main/training_args.bin
- Command line
-
hf download hf://tomlobato/prop_class/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/tomlobato/prop_class/resolve/main/training_args.bin
4.86 kB
- Xet hash:
- e00cfdf320bebc3a782a5ce5adb2290d34ee589ab1113c426a87179b35145fc3
- Size of remote file:
- 4.86 kB
- SHA256:
- b79bfcfcb956b33280413413759e215a3ca950ba1778e093d7442f961746fd7b
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