Instructions to use Jeevesh8/roberta_base_qqp_ft_36 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_36 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_36")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_36") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_36") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b8bff2dd4b579a4785c5870e4530422603a3c4950d05d7565e36cefe25eae654
- Size of remote file:
- 499 MB
- SHA256:
- be147bd121b37f3e7898af388bb457483fa63b71def2476144b76b04e1e62341
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