Instructions to use textattack/distilbert-base-uncased-WNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/distilbert-base-uncased-WNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/distilbert-base-uncased-WNLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/distilbert-base-uncased-WNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/distilbert-base-uncased-WNLI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a797cfe3749dd35ff4960e2b585e9cca3ff79f81066e90a38b94c84e15c7e994
- Size of remote file:
- 268 MB
- SHA256:
- c823fcb0fddacc7d2b3d92f6d70816e8a99d828daf029ebf274add9d4a00cb7d
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