Instructions to use Jeevesh8/roberta_base_qqp_ft_49 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_49 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_49")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_49") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_49") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dfaee475f0e5158527cc2ae242dbbd5427e3e4fb45698a0f381b89df014c0115
- Size of remote file:
- 499 MB
- SHA256:
- f497c5440110935ebb6e2a74adba53bd739f12091d84ae109e650baadac3a63b
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