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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e9a5a48e3447371cc7923eef6f5f64e6d98ae6240264e06e0b92d21ce3480c44
- Size of remote file:
- 433 MB
- SHA256:
- 5fec2009cefebea51eac3f842316cf88956c4f21df450c6d4fc47073467abf68
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