Instructions to use Jeevesh8/roberta_base_qqp_ft_66 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_66 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_66")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_66") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_66") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d346619bc0533eabe7515d17432c149954e67af51609aec6ae876dcca26df503
- Size of remote file:
- 499 MB
- SHA256:
- 9aec4c6e7d4e5edbfdce668a83f74e58e74b65729ca03fd29b2aca085cb2b7e7
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