Instructions to use textattack/bert-base-uncased-snli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-snli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-snli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-snli") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-snli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b696d9a244df9e0a8e3c791761ac01007bf80586166b85741c1d03bcc240dcd5
- Size of remote file:
- 438 MB
- SHA256:
- 7ffbed4f5e42c47db2b76d3f957c1daba3885063d2d8118c3d7b520fdf0d50a8
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