Instructions to use Jeevesh8/roberta_base_qqp_ft_14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_14")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_14") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_14") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae7c37e54d405c6134067ff0845317b03ab432f5bf6f4bbfb6e1c048d8d4b218
- Size of remote file:
- 499 MB
- SHA256:
- 46e1f00a82edfe4017a0afad03d94265aa46941a6ee5a6ec4283948e32773f5a
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