Instructions to use textattack/bert-base-uncased-STS-B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-STS-B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-STS-B")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-STS-B") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-STS-B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 486938a698c8888c5a46f65c861ec6f012e0f48a6194ff472365ac039eb8466c
- Size of remote file:
- 438 MB
- SHA256:
- 5088119e2d29035970b23c3caea3c91b912caaf3cc802ba5e4d2052e15cc0c5f
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