Instructions to use Jeevesh8/bt__qqp_ft_30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_30")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_30") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_30") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ef40c9996d6694a94991dda3336aff6fc247b87080b0201f437e464a8364391f
- Size of remote file:
- 17.5 MB
- SHA256:
- dee4f4b8c8643796d0b0f272fe5fc723e6ffb5981ebceae5dbd19462f7b3ae31
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