Instructions to use Jeevesh8/bs__qqp_ft_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bs__qqp_ft_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bs__qqp_ft_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bs__qqp_ft_2") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bs__qqp_ft_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 64fae2ca170512808616dcfc14aaced52b4193fe3d3db9561c134a3c96a5627f
- Size of remote file:
- 115 MB
- SHA256:
- 7c158b7f5fe14c8da4e3a57b4867609605820e570964d3f4950b1213ad7ef0f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.