Instructions to use A-bhimany-u08/bert-base-cased-qqp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use A-bhimany-u08/bert-base-cased-qqp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="A-bhimany-u08/bert-base-cased-qqp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("A-bhimany-u08/bert-base-cased-qqp") model = AutoModelForSequenceClassification.from_pretrained("A-bhimany-u08/bert-base-cased-qqp") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ae5bda1153a29f55d0a2373b9e0aece137957dc9303ff3c1a5a63d71b577fa51
- Size of remote file:
- 433 MB
- SHA256:
- 995bcf71eb7c885b433376d453168ae697b9a309c057f3816ebb439660d5f594
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