Instructions to use Jeevesh8/roberta_base_qqp_ft_65 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_65 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_65")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_65") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_65") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41e7ca9281bea887ecfd757c60dd83a3b2ddfe36d7b9cae2d61ddc23face3ec8
- Size of remote file:
- 499 MB
- SHA256:
- cab387bafcabc1c78612de628e0e2a4b1804d53813b9e5722149b29c83df05d6
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