Instructions to use Jeevesh8/roberta_base_qqp_ft_47 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_47 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_47")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_47") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_47") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9460921fdb9221c7c1e2a590abd3eeebdd82d2d1e68c029048ffaf44789a4bba
- Size of remote file:
- 499 MB
- SHA256:
- 12ff34ca33a155ea32594cd6990005eaada08c33a7fbf6a06ae6f1c961eceac6
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