Instructions to use Jeevesh8/bt__qqp_ft_9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_9")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_9") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_9") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6b1b024bf81071556c756d60c0734810ca488c0a14bfc2a7eb2f532241a5f371
- Size of remote file:
- 17.5 MB
- SHA256:
- ffa674c1897fbd04f1cf3d78da9b3865243bcf1536eb0cb0165f719d1eb1e6b0
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