Instructions to use Jeevesh8/roberta_base_qqp_ft_57 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_57 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_57")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_57") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_57") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11a0a2623be202982328b6fb8d676f15724260a5e08e71dcf92b13272cfa22b0
- Size of remote file:
- 499 MB
- SHA256:
- ada035d2c8e8d355ecd6d096aebc61fac8729d0c534817ba31219bc237000bc1
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