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:
- bdaca9fbdbc866883435c9cea334997d91241812886c8d9d0642121bfb9c8729
- Size of remote file:
- 627 Bytes
- SHA256:
- dd962cfcecec67841abeb990001e78b5eca24487a69228bb356e033e5c74e15f
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