Instructions to use Jeevesh8/roberta_base_qqp_ft_8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_8 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_8")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_8") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_8") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 74fd00467c21401efc9c860541a17914d38c04dc80625e1a3ea00f7d70a8fd79
- Size of remote file:
- 499 MB
- SHA256:
- d33d4e906979c7dd85f058819ea41b4b64a21b946338cb4fdc174358a3fa4737
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