Instructions to use Jeevesh8/bs__qqp_ft_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bs__qqp_ft_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bs__qqp_ft_1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bs__qqp_ft_1") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bs__qqp_ft_1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e38fccc3de69ff32750fe0ac4e0f6418e5f98836c42eab642e83f565f6e7dac3
- Size of remote file:
- 115 MB
- SHA256:
- eac53e1e7387e802e42efd59eea394fdbf580d49a7dc0e786e5f07d363a7836c
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