Instructions to use Jeevesh8/roberta_base_qqp_ft_35 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_35 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_35")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_35") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_35") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77b3d0ba6d4438cb4948f6899a51951fd7d242d7a8973659d3c8d915ca9d8f37
- Size of remote file:
- 499 MB
- SHA256:
- 1488fcd689ceab7101c6744fc57df64a32a68e6b36253d7ccf6f73a5ba5b0fbc
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