Instructions to use Jeevesh8/bt__qqp_ft_23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_23")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_23") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_23") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9e523f7f7329119e7b0295612c58f450998879467173b7de335335c879062d26
- Size of remote file:
- 17.5 MB
- SHA256:
- 0bda93e5d5a555e4d6e3f6d073f7c84aa6b3d5ae1db20c31c6a5c5118f67887d
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