Instructions to use Andranik/TestPytorchClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andranik/TestPytorchClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Andranik/TestPytorchClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Andranik/TestPytorchClassification") model = AutoModelForSequenceClassification.from_pretrained("Andranik/TestPytorchClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b03c9c993834aae541e1ccb7f0e2a166e7c724ff4adad99ca46088017c73c860
- Size of remote file:
- 3.06 kB
- SHA256:
- 3e32b2a478052e123b9c862f0c0c34c6fb1d7c937d03b560a66d6e5b16990166
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