Instructions to use Jeevesh8/roberta_base_qqp_ft_33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_33 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_33")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_33") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_33") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1a0c3719ed6f03724cb00f3a9d1365b0369de3019f10448d5ade0f7e380e608
- Size of remote file:
- 499 MB
- SHA256:
- c4edc781b99cf1406a8e411a707c3c744fd4675b5ca905832159f135d7d0f6a8
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