Instructions to use Jeevesh8/bt__qqp_ft_28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_28")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_28") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_28") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d722b028a74a2ea37fcf03fe573b9c11072890276a2b3a7bb442d2ddad3dd20a
- Size of remote file:
- 17.5 MB
- SHA256:
- 900c739b8796a49f132f8bcce614e6add2db9649dc5bc4d639bb6d09ea56f214
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