Instructions to use Jeevesh8/init_bert_ft_qqp-77 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-77 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-77")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-77") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-77") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8fb3360c8844e65e6958bd18d03e730f79c2920a11a8445b6408e65b3a9272c8
- Size of remote file:
- 438 MB
- SHA256:
- d12f288533004af03ad7d405cf1304ec8a280b1a4762560fdf0e380a49802a14
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