Instructions to use CogComp/bart-faithful-summary-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CogComp/bart-faithful-summary-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CogComp/bart-faithful-summary-detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CogComp/bart-faithful-summary-detector") model = AutoModelForSequenceClassification.from_pretrained("CogComp/bart-faithful-summary-detector") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 423b13f9390c650de8f5a53df578a52ea492106b6c9e67a5fd49d9c3aa4b42a8
- Size of remote file:
- 560 MB
- SHA256:
- fdc5abb901a360d7f6bbd39925c3db787a3f88d4580531d02de2260defd155fa
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