Instructions to use handraise-dev/test-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use handraise-dev/test-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="handraise-dev/test-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("handraise-dev/test-model") model = AutoModelForSequenceClassification.from_pretrained("handraise-dev/test-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18a393bb7d747f6a5ee84f4bb269d04cd3a545827c507c1422375b8ce6bfd7f7
- Size of remote file:
- 4.92 kB
- SHA256:
- ac429ebc474a15ae5ccd180b965f0d5cc6f90862ba37dad8389f26476334c3eb
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