Instructions to use Jeevesh8/bt__qqp_ft_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_1") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ec6a9a49844e0cbaead72d98471fb0e90280ef9459b8d3e46f98b160ca155b2
- Size of remote file:
- 17.5 MB
- SHA256:
- df965153890085c6308c2f0054b59eb56ed93a079a9529deb682ec6cb55457fa
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