Instructions to use Jeevesh8/roberta_base_qqp_ft_59 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_59 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_59")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_59") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_59") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7edc7cd0e7d60ddd375103d8141f0b85c6359d5894f5bea0945d42928fe36e32
- Size of remote file:
- 499 MB
- SHA256:
- 9dce63750bb5b0b61d507515090eca2419678fc3008da68a28e651e5c96914a2
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