Instructions to use amanda-901014/deberta-hard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amanda-901014/deberta-hard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amanda-901014/deberta-hard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amanda-901014/deberta-hard") model = AutoModelForSequenceClassification.from_pretrained("amanda-901014/deberta-hard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amanda-901014/deberta-hard: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/amanda-901014/deberta-hard/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amanda-901014/deberta-hard/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amanda-901014/deberta-hard/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 1943b5828ff199ce98db66615469c37a8ed13507f75239635a71b0b479a0c1f9
- Size of remote file:
- 557 MB
- SHA256:
- 6242f2bf656555640910a806a62509c7281f71fe3441a63952c549dff296cafa
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