Instructions to use Jeevesh8/roberta_base_qqp_ft_61 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_61 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_61")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_61") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_61") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 515eb0d953d7723e702abff42a07cfda53ca30970d49cfed0f28bfaeecebf378
- Size of remote file:
- 499 MB
- SHA256:
- 586c36723417f954c2e9dfee433b482e3d6f2fea4df0f4e34ab0862c5ad0ff62
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