Instructions to use Jeevesh8/bt__qqp_ft_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_10")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_10") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_10") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e62c60b6766f239284c066ba5b4aa60046c87a829fbdd78c4394849757582fe
- Size of remote file:
- 17.5 MB
- SHA256:
- b501ef74e5d03f8176bcbaed098cad483b281aea6b91b51eea834e8e707c8e7e
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