Instructions to use dpravinv/fold_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpravinv/fold_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dpravinv/fold_0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dpravinv/fold_0") model = AutoModelForSequenceClassification.from_pretrained("dpravinv/fold_0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dpravinv/fold_0: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/dpravinv/fold_0/resolve/main/training_args.bin
- Command line
-
hf download hf://dpravinv/fold_0/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dpravinv/fold_0/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 0c6197b805b873817ffe17bca94f01852b918cc16d074e3e60527343f78f16a5
- Size of remote file:
- 5.37 kB
- SHA256:
- c02dfc7b956c7a4c519ba888f79aec6174bdeb90be13637f2740c1ffa344c26d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.