Instructions to use Jeevesh8/bert_ft_qqp-64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-64 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-64")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-64") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-64") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad29af5e35d9717927b69443fd62526fad320d5f9af522ccfed7c3e5af6d17db
- Size of remote file:
- 438 MB
- SHA256:
- 8a4f4284d91895ccb8e31dcbb921fde99ca541f75c047a878092b6f0791ff79a
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