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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e70229d35885f3e4f1118937bfdb6f6081ae5085e73f9ef8de049d3b268e75af
- Size of remote file:
- 627 Bytes
- SHA256:
- 0e2e92bffa9a9994dd4078c4eb7755252a90f9625046ad86ce8f39b86df49bde
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