Instructions to use Jeevesh8/roberta_base_qqp_ft_51 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_51 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_51")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_51") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_51") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49065dfe91dc24d81d12376cb2d7a05e62020c931f6617e69d40e28768b9d6ab
- Size of remote file:
- 499 MB
- SHA256:
- 92eb223b6d0cfee669dc3761a9e1bbb2b3cf75dc54b6b52bfbdd5c556f622166
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