Instructions to use Jeevesh8/roberta_base_qqp_ft_24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_24")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_24") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_24") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5eca9bd3acf0408e71531340b55485cdfd379a343f8e08f2ede58e37e0dbb22b
- Size of remote file:
- 499 MB
- SHA256:
- d7dcd58829fc2d191b9322553c754ed25eff851cf20153f0760b6d9fe18cdc94
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