Instructions to use textattack/bert-base-uncased-WNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-WNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-WNLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-WNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-WNLI") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b949a0bce9ec604c52743df292aa0e3ae51f3856a7fe6fa13c95833b75fb3739
- Size of remote file:
- 438 MB
- SHA256:
- aff283d803d5b3410824d07f73b94cc0885b218bb26d1fd2e6561467be031e13
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