Instructions to use Jeevesh8/roberta_base_qqp_ft_26 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_26 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_26")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_26") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_26") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acee31a4ba5d889d70bfec238800a9b05bb5a2789dbf6e0c1c0662559d965217
- Size of remote file:
- 499 MB
- SHA256:
- a2733ebcae24d8be36c5cdc2c8c449b8468a7c898bc5d32f3a147cd976b6ad05
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