Instructions to use Jeevesh8/roberta_base_qqp_ft_46 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_46 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_46")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_46") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_46") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57ef20e4f7fd9d025147af9e58d21bace969967818b86402e367e44fedb1f9bd
- Size of remote file:
- 499 MB
- SHA256:
- 015b6f52d4b7204725c22ee7dabc13a092a2b497f34d2207bdce29ddbeec6ec1
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