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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- baa25f61f94f28a41a7faea793dd7d45cb1489f9748025ee1e5f96f288e7c547
- Size of remote file:
- 438 MB
- SHA256:
- db8f7b9402f969b248151a8f0379de2de11e63c7f71181f6c3b3c9792d655480
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