Instructions to use Jeevesh8/roberta_base_qqp_ft_34 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_34 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_34")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_34") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_34") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65a438edd6bf3d3eb11734176e0b2bb90a37c9458a7694bccb96293633923f57
- Size of remote file:
- 499 MB
- SHA256:
- ae91e8a20e93f863e34b6cd41692b9bb10ee81a4e8de36960560eb33243d1788
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