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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fff4d22d0c0fa2e8016720f5ef086afbd7d63a9437350d93efdd24316bb77f16
- Size of remote file:
- 351 kB
- SHA256:
- 7f94007e139edc704c061e1e27524ae135b77994a05b23f11968d3866fc01ed2
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