Instructions to use Jeevesh8/roberta_base_qqp_ft_16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_16")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_16") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e1cf9e1025cd4aa8dc8b8cc231a242bbc4f416f374333b44dcb476a1da4e313
- Size of remote file:
- 499 MB
- SHA256:
- ede43bf77bcb62c171527549a88cebb80d973b261e4e3927c86a3ae7b40d8f2b
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