Instructions to use Jeevesh8/roberta_base_qqp_ft_22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_22")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_22") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_22") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 889ba55c8e4a7eb5052a8293563ba4918971c50fbc552b8d93dc984cb85ccd1f
- Size of remote file:
- 499 MB
- SHA256:
- 802e8d5e6aaa22f796360f66457b3e7a58cc35859f77bf7a139c2c3c57d8863a
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