Instructions to use hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-DebertaV2ForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c48c1abd7085b1478a6bc89206d61e9e9e894809b748fd177b489787c2f7c19
- Size of remote file:
- 16.6 MB
- SHA256:
- fafecbf0d68eea4ca064863cdd4ec0cc52c14c80410130fd00e696fe26efe3ce
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