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 tf_model.h5 from ydshieh/tiny-random-DebertaForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 452 kB
-
https://huggingface.co/ydshieh/tiny-random-DebertaForSequenceClassification/resolve/refs%2Fpr%2F5/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-DebertaForSequenceClassification@refs/pr/5/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-DebertaForSequenceClassification/resolve/refs%2Fpr%2F5/tf_model.h5
452 kB
- Xet hash:
- 062b7d84ce726eeb39f2adfa9687186f385edd4b2217961cf8e7aaa4007c0a3e
- Size of remote file:
- 452 kB
- SHA256:
- d39618802910ba9fa447114c076d1fb8d259d53ebd9c1276e5eeb75ab35cc2d0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.