Instructions to use Jeevesh8/roberta_base_qqp_ft_42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_42 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_42")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_42") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_42") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba4cac3a1c6c9ec28d99fe08ee7bf85ff9c15128a91fc58566a318286f8ce6d8
- Size of remote file:
- 499 MB
- SHA256:
- 6be4ac984689870de38a1c2b560150750c5e3fccfbbf8878740a312d5fe41af9
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