Instructions to use Jeevesh8/roberta_base_qqp_ft_48 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_48 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_48")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_48") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_48") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53885e11313614dabe8c93cc17c8c32acbe5750149061b9d144447b06bdce233
- Size of remote file:
- 499 MB
- SHA256:
- 2d7d19335841cf0213ba1890f883a604f5633910132e9cad13898daa94bcb73e
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