Instructions to use Shruthikaa/FNet_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shruthikaa/FNet_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Shruthikaa/FNet_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Shruthikaa/FNet_Classification") model = AutoModelForSequenceClassification.from_pretrained("Shruthikaa/FNet_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b1e007ca208c877c054eefb2af94c6ce4aa65ba4f85323ef285e55388130f20
- Size of remote file:
- 331 MB
- SHA256:
- 9bf120466363434e30732de78fe8bac908c63b8c9d0be9514f76ead69139b9ca
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