Instructions to use Jeevesh8/roberta_base_qqp_ft_27 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_27 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_27")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_27") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_27") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee119a8abb82242bf91d1ba9eea84e606c0551dfe950260c72b318575d421b93
- Size of remote file:
- 499 MB
- SHA256:
- 05d0d68e2097613655c9e989d80c7d39d4b5390cf7c5f77705e11d05dc11d91d
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