Instructions to use Jeevesh8/bt__qqp_ft_12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_12")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_12") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_12") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3d98f145c917fffab958f4da18786c178f2057638ee6f397f0eff0f7470dee74
- Size of remote file:
- 17.5 MB
- SHA256:
- d21ece4e03191cd0ab4c2c57a0b9d99a7a1ba104ed6b8ccfadb38da65cf3eedb
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