Instructions to use hf-tiny-model-private/tiny-random-DebertaForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-DebertaForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-DebertaForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-DebertaForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63d9848db1287b0c46d621f3ae92552ab9d5ca06e80be4231d6a0f8e1cfaa29b
- Size of remote file:
- 351 kB
- SHA256:
- 14f0a29dcfad94992c1a8efca6118cc2a98795e5fc7dab8d32c780d603c35fec
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