Instructions to use amanda-901014/deberta-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amanda-901014/deberta-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amanda-901014/deberta-medium")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amanda-901014/deberta-medium") model = AutoModelForSequenceClassification.from_pretrained("amanda-901014/deberta-medium", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amanda-901014/deberta-medium: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/amanda-901014/deberta-medium/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amanda-901014/deberta-medium/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amanda-901014/deberta-medium/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 7539b3be10aed1c4949ccdfeac1f8ca92d5582f9558946f29fb91df82088a0dd
- Size of remote file:
- 557 MB
- SHA256:
- 0328040998892be6dfca2c9ce68cdda3e82470a607b3785835fd5fb95388bf00
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