Instructions to use hf-internal-testing/tiny-random-DebertaForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DebertaForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-DebertaForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-DebertaForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-DebertaForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cf9957a4f0738119f8fdd8180cc974ac92fdfdd55c6ee171b9a41bb803bd92ae
- Size of remote file:
- 452 kB
- SHA256:
- 250b54e31876249dedfb5b55b9e16ab616cc1a6b70a989e15369a922d1a23249
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