Instructions to use textattack/bert-base-cased-STS-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-cased-STS-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-cased-STS-B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-cased-STS-B") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-cased-STS-B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96f980d383ec7541b019d55251d38264950073987bbd1ca132baebe221dfb7cf
- Size of remote file:
- 433 MB
- SHA256:
- a86d413f3d0daf20e2fcc64f19bf5e7465c0ee9a5ad4a85b5aaf4aeb0ec3bac4
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