Instructions to use Jeevesh8/bert_ft_qqp-33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-33 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-33")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-33") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-33") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3d3eef1a648f417e5c0dfdb2b4f78ca47f2ac40c49ad2ab1502e6557e4ac816
- Size of remote file:
- 438 MB
- SHA256:
- 74b33ede251e010556507fa68ef6b506fb5cdbaf9636289275ff507f4b80b082
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