Instructions to use Jeevesh8/roberta_base_qqp_ft_68 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_68 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_68")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_68") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_68") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93fde93c7325ca35e2be5950b6fdc367e9cf6cd60ef2d567777b060fe2cd8781
- Size of remote file:
- 499 MB
- SHA256:
- 7673c0a24672212b6bf6af0ac551893964c097f77c43002c84f774aedf8e23b1
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