Instructions to use amanda-901014/deberta-easy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amanda-901014/deberta-easy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="amanda-901014/deberta-easy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("amanda-901014/deberta-easy") model = AutoModelForSequenceClassification.from_pretrained("amanda-901014/deberta-easy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from amanda-901014/deberta-easy: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/amanda-901014/deberta-easy/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://amanda-901014/deberta-easy/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/amanda-901014/deberta-easy/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 645e2702e5c865469bb536ba8fae9c273e6c941426a32e0a5a59a44b1f7037e0
- Size of remote file:
- 557 MB
- SHA256:
- ac2ef00f04feed929a5660973906028c8397515e126a466e2b183040427936a0
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