Instructions to use jayavibhav/deberta-classification-10ksamples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jayavibhav/deberta-classification-10ksamples with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jayavibhav/deberta-classification-10ksamples")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jayavibhav/deberta-classification-10ksamples") model = AutoModelForSequenceClassification.from_pretrained("jayavibhav/deberta-classification-10ksamples", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from jayavibhav/deberta-classification-10ksamples: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/jayavibhav/deberta-classification-10ksamples/resolve/main/training_args.bin
- Command line
-
hf download hf://jayavibhav/deberta-classification-10ksamples/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jayavibhav/deberta-classification-10ksamples/resolve/main/training_args.bin
3.96 kB
- Xet hash:
- 4fc0f5f4d4d4668fdd3ab506a19d395256c42d43462617efd772cb8ea2dca0bb
- Size of remote file:
- 3.96 kB
- SHA256:
- ce07f47f25e83cf7466216c0e721b32d47a3d23c5dca2a30c711314cbe7e6cba
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