Instructions to use dpravinv/fold_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpravinv/fold_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dpravinv/fold_3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dpravinv/fold_3") model = AutoModelForSequenceClassification.from_pretrained("dpravinv/fold_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dpravinv/fold_3: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/dpravinv/fold_3/resolve/main/training_args.bin
- Command line
-
hf download hf://dpravinv/fold_3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dpravinv/fold_3/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 904233e8e6a67b1745f6f229a76f7cb941e1d0d27fdcb5f90311ece334962cba
- Size of remote file:
- 5.43 kB
- SHA256:
- 609998671f3b3e2b3d310560c91dfcfd94601c79aa12946d99d9d80a0d331b92
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