Instructions to use Jeevesh8/bt__qqp_ft_16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_16")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_16") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_16") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ef8d4c52f52b38c4ce4b49e67e0f7c35edd2809991d48a11c1c1da0071a5cc4
- Size of remote file:
- 17.5 MB
- SHA256:
- 3cbc202bcd2f534c715ddba5255c2c99ded5f28746c167317bc56d2a706440af
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