Adversarially-filtered commonsense inference — pick the correct sentence ending.
Browse commercial NLP / Text → Visit original source ↗HellaSwag tests commonsense natural language inference by asking models to choose the most plausible ending for a short context passage. Passages are drawn from ActivityNet captions and WikiHow, and distractor endings are adversarially filtered via Adversarial Filtering (AF) so that they're easy for humans (>95%) but hard for earlier BERT-era models. Remains a standard LLM eval today despite saturation at the frontier.
LQS is our 7-dimension quality score, computed from the dataset's published statistics. See methodology →
Composite score computed from the 7 dimensions below: completeness, uniqueness, validation health, size adequacy, format compliance, label density, and class balance.
Common tasks and benchmarks where HellaSwag — Commonsense NLI is the default or competitive choice.
What's actually in the dataset — from the maintainer's published stats.
HellaSwag — Commonsense NLI is distributed under MIT. This is a third-party public dataset; LabelSets indexes and scores it but does not host or redistribute the data. Always verify current license terms with the maintainer before commercial use.
LabelSets sellers offer paid nlp / text datasets with what public datasets often can't give you:
Other entries in the NLP / Text catalog.