Instructions to use Jeevesh8/bt__qqp_ft_19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_19 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_19")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_19") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_19") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1fadb7fad26b3cc430a262f4390138d1723b8a9b8fbdcb4d0c63dad134170165
- Size of remote file:
- 17.5 MB
- SHA256:
- 6e72cdaa1e4cbd25e5540962e16c1edfac09a23817b85f4643b742972343d818
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