Instructions to use Jeevesh8/roberta_base_qqp_ft_55 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_55 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_55")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_55") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_55") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75a705d07e3150b603442c74623797e099b3b8c829c3ebe9b62c5cbd13975102
- Size of remote file:
- 499 MB
- SHA256:
- 909f92b3cbf36314a25cde000f9d59b0a10be40620ade66a95baca8ce66e0c26
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