Instructions to use Jeevesh8/roberta_base_qqp_ft_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_10")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_10") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_10") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e8ca8f386aeda6aeef72a080805210ec0a73b7add7ced64e3eb0309f92dda9d
- Size of remote file:
- 499 MB
- SHA256:
- bbbc024fe556cffc2f1b0bde9a406b84b14606325a47e7e64af781eb4aa8e316
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