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:
- dcb266912881710cc5e66a2a6d5c88bd2d16f703679b1a3c44528df092c75d36
- Size of remote file:
- 711 MB
- SHA256:
- b7824a2bd6ee1e24f65f50e4d6632766b7826d0627b83ce0c9f4dfc176ae001a
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