Instructions to use Jeevesh8/roberta_base_qqp_ft_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_5") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb0cf7af11f61ec2c6e1bb936a9927d44a24cca8137095fc37bf97c9e677d44e
- Size of remote file:
- 499 MB
- SHA256:
- e804d86cecb162e2a641132372af846415eeff593f73de4395996a5ce266ea21
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