Instructions to use Jeevesh8/bt__qqp_ft_25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_25")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_25") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_25") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3776bdd66f9780a1c7d0b4a14f596156c5d845c72907cdac85d5ee0f50a3a9d5
- Size of remote file:
- 17.5 MB
- SHA256:
- 0f4283ff3c85d14d88bee73e0eb0ba395a5a87d7268b300cec3321d25496a887
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