Instructions to use Jeevesh8/roberta_base_qqp_ft_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_6")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_6") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_6") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b60d718e508f2f0079592173ff61862cd5e0b9cab973b8b8ab69c2eca906ce71
- Size of remote file:
- 499 MB
- SHA256:
- ae2c270bd8a49bb1cc315b868d104d580044f3878b399ab6f6e5f6c797ec27a3
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