Instructions to use Jeevesh8/bt__qqp_ft_22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_22")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_22") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_22") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f3c9add5bd42d9da01d174aec9a2acd73bee1d0af116e23263a05f681d6d4ae
- Size of remote file:
- 17.5 MB
- SHA256:
- 0829892e5749da120b2ff06547eaa218e8f3cb5c323acf240cd8439a96df1d47
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