Instructions to use Jeevesh8/bt__qqp_ft_20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bt__qqp_ft_20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bt__qqp_ft_20")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bt__qqp_ft_20") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bt__qqp_ft_20") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f5ec7e47569cd92cef1499d20f13b4a87caeaf5e4714cc3d9f814830cd0a601
- Size of remote file:
- 17.5 MB
- SHA256:
- 63cf8605280e51e244adbb7ebc0a5998ee134af6cedacaa744cc3a60badb0fd2
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