Instructions to use digit82/test-model2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use digit82/test-model2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="digit82/test-model2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("digit82/test-model2") model = AutoModelForSequenceClassification.from_pretrained("digit82/test-model2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6915461de9aeccaf0361a45f3d56217950e9114bface114abe41f58ffd4f4b9f
- Size of remote file:
- 443 MB
- SHA256:
- 649694a8f3b79d624bb95f0ae8dff5919e50987e9edfe6e279e30425e2306da4
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