Instructions to use Jeevesh8/roberta_base_qqp_ft_17 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_17 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_17")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_17") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_17") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 790697e8bad63826fe50dca000f36fa29e9942e0dd724aaacffdf52718b6298d
- Size of remote file:
- 499 MB
- SHA256:
- dfb6703236e90dd52f8181d71336a78de61bf5844e632c69cc47cfa532f23ea9
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