Instructions to use Jeevesh8/roberta_base_qqp_ft_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_1") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c45f6b3ae9f48b9c54f82d1a3bc605550ce50639f03566673f5af661044fa31
- Size of remote file:
- 499 MB
- SHA256:
- 4e4ca23b35f44e6a1f32c75b95203591efc49b43d4e686516ed8fd417d3a2f1b
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