Instructions to use ydshieh/tiny-random-DebertaForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-DebertaForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ydshieh/tiny-random-DebertaForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-DebertaForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("ydshieh/tiny-random-DebertaForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ydshieh/tiny-random-DebertaForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 366 kB
-
https://huggingface.co/ydshieh/tiny-random-DebertaForSequenceClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://ydshieh/tiny-random-DebertaForSequenceClassification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ydshieh/tiny-random-DebertaForSequenceClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
366 kB
- Xet hash:
- bd009704a9cb144ad981388b416b35df73d8b98d56bd66e4bae483b110c94661
- Size of remote file:
- 366 kB
- SHA256:
- b7a9b04f66829411b333bf95fbdb1a4cda2ea5bc2b8b3ea6811775edf85fa0d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.