Instructions to use Jeevesh8/roberta_base_qqp_ft_60 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_60 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_60")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_60") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_60") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fa592beea905e723cebad9633437287fb33b325ae34f717efd4b0963028347de
- Size of remote file:
- 499 MB
- SHA256:
- a375727b4c2215ae4ee6cc8dc16c9f1f8b8dc4c69af5e8bf245e900762aa6ad1
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