Instructions to use Jeevesh8/roberta_base_qqp_ft_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_9") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_9") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31be55b27241a2f540f58ea645bc973c2a44ffff74e1f4946ac91a1770c28970
- Size of remote file:
- 499 MB
- SHA256:
- f48fa295ceb3f9e34ff819cebf3dc0c77169b2ab811718526ce943844cfdcd3a
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