Instructions to use Jeevesh8/bt__qqp_ft_26 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_26 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_26")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_26") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_26") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 400c3d1df9940c5b0cbf93e5f4486790e76b18e6dca9ae428a6483a256ae35c4
- Size of remote file:
- 17.5 MB
- SHA256:
- 23bf3d262ba180f6b5b555a13e8ddf0a83243488ef2dd68f82191ef04f58a1e9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.