Instructions to use Jeevesh8/roberta_base_qqp_ft_37 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_37 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_37")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_37") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_37") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 952ef4176d835e27637d86cdd1a716e4d6179c965cc55ce5db82048206b09942
- Size of remote file:
- 499 MB
- SHA256:
- 73d2bad5ec2ef89e29de77993446b11d383b863f2e1835721b7af1f1d0e14f49
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