Instructions to use Jeevesh8/roberta_base_qqp_ft_53 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_53 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_53")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_53") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_53") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86561c90bdc2d0b48d1b29ae3206fb85f633a32a5e3ed37264f71c422d0c734f
- Size of remote file:
- 499 MB
- SHA256:
- 960357f2fda679ce09f7a3ab92f524ca8a11a57458834fefa397aa2dede63d4c
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