Instructions to use Jeevesh8/bt__qqp_ft_27 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_27 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_27")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_27") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_27") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16c3e20926afaeb54d001f0a2985193c901a58576fbbc2c1fc9b2f7f8b5eddf1
- Size of remote file:
- 17.5 MB
- SHA256:
- 74814ed3e7e0f3474f795cd695b9386793149ecec5f4355490b3d4835de59ece
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