Instructions to use Jeevesh8/bt__qqp_ft_13 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_13 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_13")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_13") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_13") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db5dd6b400df70695d3b4ff9db73bbd5e1082c27a47df273b0bd94ee9d3ea296
- Size of remote file:
- 17.5 MB
- SHA256:
- 0cd41a97175798df3c36ffa352c887c28b38f428f08faefdba17276846f0a486
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