Instructions to use Jeevesh8/roberta_base_qqp_ft_64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_64 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_64")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_64") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_64") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e66b580ca9fcf92901f3464a85b68900107d25cddb8d51c3cc4eb8d9a1631d4
- Size of remote file:
- 499 MB
- SHA256:
- 45c9ef8f3d77bb9171461280c48a999b1426d92e2f0bbf2dbd6c77d396abade5
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