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 training_args.bin from amanda-901014/deberta-easy: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/amanda-901014/deberta-easy/resolve/main/training_args.bin
- Command line
-
hf download hf://amanda-901014/deberta-easy/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amanda-901014/deberta-easy/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- 01eca7f2bd8e6a2c7c5c21224c10290585e0d2249b360af7e8ff0712d10e366f
- Size of remote file:
- 3.25 kB
- SHA256:
- 01e1d8b87afbf2163ba29e167a68f9332413a378cca7b688db5f2aa7c1da90e8
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