Instructions to use Jeevesh8/bl__qqp_ft_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bl__qqp_ft_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bl__qqp_ft_2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bl__qqp_ft_2") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bl__qqp_ft_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 719b9f4c54cdf44940472d2747184363d426ed308e9f3f7c7a5d9244560b06a6
- Size of remote file:
- 1.34 GB
- SHA256:
- 5854234b09d3b16fcc92c1a787752cc3ec49a57fc80a0cce5c1ff55af58ee8ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.