Instructions to use breadlicker45/test-class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breadlicker45/test-class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="breadlicker45/test-class")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("breadlicker45/test-class") model = AutoModelForSequenceClassification.from_pretrained("breadlicker45/test-class") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3683672fc8220a3563e9d3c03c87d997f24a5c213efea71ca7eb39fef213f9b
- Size of remote file:
- 557 Bytes
- SHA256:
- 7fa181fa360d46feed4180ea17c8b6a4a879a9b4231c2e91aff2be20be9076cc
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