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 pytorch_model.bin from jayavibhav/deberta-classification-10ksamples: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/jayavibhav/deberta-classification-10ksamples/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jayavibhav/deberta-classification-10ksamples/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jayavibhav/deberta-classification-10ksamples/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 6b7df076c64dccee54a6cfb6e841bdc7e132389221517e49692cb2d0f2ffaf3a
- Size of remote file:
- 557 MB
- SHA256:
- 67d13224114adbd4204064d246407b0264b780e09eea8ddfece48c8168097144
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