Instructions to use Jeevesh8/bert_ft_qqp-80 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-80 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-80")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-80") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-80") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1301d2e5ed03e47204e1c2f02a1df4136cd5bca751657a5d766178d7b65a58fa
- Size of remote file:
- 438 MB
- SHA256:
- ec7cc8023953ce85c6acda785242d0a344cd89dd5266cd9523c085a21fc1afff
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