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:
- 573ef3f3cf79951d3c4d3b01a4c9fc95076efb49354e7acf9747dec2f0440a80
- Size of remote file:
- 14.6 kB
- SHA256:
- 972139d83957a9cf2600cb6eeca17287d7a5377c33a53500ae7e13fe830ad36b
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