Instructions to use Jeevesh8/roberta_base_qqp_ft_31 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_31 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_31")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_31") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_31") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42bf2730c930918f30498fec8eee5e6a0bdd6bc2c77a91c41725f2dce6f993cd
- Size of remote file:
- 499 MB
- SHA256:
- 0e71a9bf62997ab33310fc445946a87a0575ff196a44f2b3d475e49c1f981dc1
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