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 training_args.bin from amanda-901014/deberta-hard: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/amanda-901014/deberta-hard/resolve/main/training_args.bin
- Command line
-
hf download hf://amanda-901014/deberta-hard/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amanda-901014/deberta-hard/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- 48b460cf48d644904de20fe7f1572758c9b7ad16fbb3eb3eb0079cbaf095eb8c
- Size of remote file:
- 3.25 kB
- SHA256:
- a419cf65b278539ed79e2bf81a6323752179309de672de689b100f2fae84526d
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