Instructions to use dpravinv/fold_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dpravinv/fold_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dpravinv/fold_1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dpravinv/fold_1") model = AutoModelForSequenceClassification.from_pretrained("dpravinv/fold_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from dpravinv/fold_1: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/dpravinv/fold_1/resolve/main/training_args.bin
- Command line
-
hf download hf://dpravinv/fold_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dpravinv/fold_1/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 38f0a8260cc2182c99f140ec57068ef24d04b26fb22eda033b7b90cc073d261f
- Size of remote file:
- 5.43 kB
- SHA256:
- 2b575cc4055fd74b99c462a89dd77496ca692d618ca8035fc86dc50bb16b21e7
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