Instructions to use Jeevesh8/roberta_base_qqp_ft_23 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_23 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_23")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_23") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_23") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 09c180433e7a394036a17c2d9fcf5ee9274e2270097b9eb9e960ddb60f92cd91
- Size of remote file:
- 499 MB
- SHA256:
- 0c707c901c06a57bb27e0f42fe48fae9414a542c243538ef3e8319fc6bbe8870
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