Instructions to use Jeevesh8/roberta_base_qqp_ft_58 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_58 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_58")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_58") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_58") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 602729e5ab9b69720d8c435b99b26fb288447ae6fe666eb8a28d755ade73265d
- Size of remote file:
- 499 MB
- SHA256:
- dd1999dd644ef947fbed783191224acf4624147b587e11a7c79f3ae1198cdfdb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.