Instructions to use Jeevesh8/roberta_base_qqp_ft_52 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_52 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_52")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_52") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_52") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed1f36d0b0df7feec22a65b81fa9e8ab7d2c370d9a27b3ae42de8dc85cb0910e
- Size of remote file:
- 499 MB
- SHA256:
- 5bb1ba0b3d328ab340f23eeae5664c71539fcdc851c1630cb32f2f4b7e08f4a9
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