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:
- 76143eb6afed36eaef6f35ad2b6e459da8f5bfaa219beedc7ee8c1668b09d6c3
- Size of remote file:
- 1.05 kB
- SHA256:
- d62f9fc02d483cebe3fc3da6545fd6d5eabaa32bab1446411a81016a631bb01e
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