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:
- b2cc40432044b629b2e450aed5eba5c67c2fa881c85c5c7cd48833261628b806
- Size of remote file:
- 268 MB
- SHA256:
- 8b8f7e48b162a4aac5c244c02fbb7a515e079c6380c5ac8e30bd060fef6e6620
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