Instructions to use Jeevesh8/roberta_base_qqp_ft_63 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_63 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_63")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_63") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_63") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18ba409eed797ce630ce44b331f98b3329aad2cb30a78e523d657a51c5510235
- Size of remote file:
- 499 MB
- SHA256:
- 712c6960973a9242a1b6102d47a7052fcf9759c4b0a991b27c4abeef7a4c83ca
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