Instructions to use Jeevesh8/bt__qqp_ft_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_7") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_7") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7066886b1d70654fdd29d9b614d69c29ebc11a85b4d5ea3fcfc0d36751209896
- Size of remote file:
- 17.5 MB
- SHA256:
- 1bd0f8587735327d24d2821188f4e4de8e08c80d59248331ffe35a6c54a4be7d
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