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/main/tf_model.h5
- Command line
-
hf download hf://ydshieh/tiny-random-DebertaForSequenceClassification/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/ydshieh/tiny-random-DebertaForSequenceClassification/resolve/main/tf_model.h5
452 kB
- Xet hash:
- addc7e5dc5544ea16b6c3e5a668b117f0461049586c044e0d933d70c3c6ecac9
- Size of remote file:
- 452 kB
- SHA256:
- 84b857adb354600f4756f49af9c889f9992ebdd16b58a57eb0598f32eb4de443
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