Instructions to use Jeevesh8/bert_ft_qqp-63 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-63 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-63")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-63") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-63") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3740f77342206c98b0413275b6645054230d568c4191eace61f4438c212c78d
- Size of remote file:
- 438 MB
- SHA256:
- 7bcedb4cb81777085ebeaddf8dffe138d6576f5423aca5c51aab4f84fcf0a914
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