Instructions to use Jeevesh8/roberta_base_qqp_ft_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_4") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6c7f954c333f25f3c62f3e08c5775559e3a45b9aef89182e9be765f2f7ec14d0
- Size of remote file:
- 499 MB
- SHA256:
- 749fcecfc23a138a25371cd4bdf26beee18af26802ec1f4068b8ca2589fb07c8
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