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 training_args.bin from amanda-901014/deberta-medium: direct link, hf CLI and curl.
- Browser
- Download file 3.25 kB
-
https://huggingface.co/amanda-901014/deberta-medium/resolve/main/training_args.bin
- Command line
-
hf download hf://amanda-901014/deberta-medium/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/amanda-901014/deberta-medium/resolve/main/training_args.bin
3.25 kB
- Xet hash:
- 9383c23feba44086482f930690fdadcbf1225b657aab796c86c445214c291309
- Size of remote file:
- 3.25 kB
- SHA256:
- 5b34fb27d29214ee290286c0df86d3188d3da0747f10baacf988f3c3dffeb32f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.