Instructions to use Jeevesh8/bt__qqp_ft_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_3") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ab3031308c4678c2ff8d863e3e863925865259a0f22064797bfc3d762b362b8
- Size of remote file:
- 17.5 MB
- SHA256:
- ed75a2d57c1d50842a16cf2f483c0124789f36ea57628b54715e0d748176784b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.