Instructions to use Jeevesh8/bt__qqp_ft_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_2") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6497247cbad57d7a9cff93b100e8266fbcc05f0f1fdf229b2f45b832920b222a
- Size of remote file:
- 17.5 MB
- SHA256:
- 0113cf7e7ec20b18b731599e8b50457d72424aaee093f42e7d5a83315eba93ea
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