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:
- 63ba8d6c26515b81df49b7501f40f5e1a5d9f1a1f239e383341f327c4a902e38
- Size of remote file:
- 819 Bytes
- SHA256:
- 6dee5080074f9b1acf8ca740f9a52200c5fc8ddd22c15afad486d9ee076d7674
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