Instructions to use Jeevesh8/roberta_base_qqp_ft_56 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_56 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_56")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_56") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_56") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 67b5bbf6572f9b2efa6698bc121246129846a161852b700bcd581699709786e5
- Size of remote file:
- 499 MB
- SHA256:
- f1a308b690c3f79764f4f9ce77ff244cb014b288096a1d6e309316cebc804f27
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