{"url":"/dataset/e-vil","name":"e-ViL","full_name":null,"description_markdown":"**e-ViL** is a benchmark for explainable vision-language tasks. e-ViL spans across three datasets of human-written NLEs (natural language explanations), and provides a unified evaluation framework that is designed to be re-usable for future works.\r\n\r\nThis benchmark uses the following datasets: [e-SNLI-VE](e-snli-ve), [VCR](vcr), VQA-X.","description_withheld":null,"homepage":"https://github.com/maximek3/e-ViL","introduced_date":"2021-05-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/e-vil-a-dataset-and-benchmark-for-natural","title":"e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Tasks","first_author":"Maxime Kayser","url":null},"license":{"name":"Multiple licenses","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Explainable artificial intelligence","url":"/task/explainable-artificial-intelligence","datasets_with_task":"/datasets/task/explainable-artificial-intelligence"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["e-ViL"],"data_loaders":[{"repo":"https://github.com/maximek3/e-ViL","url":"https://github.com/maximek3/e-ViL","frameworks":[]}],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}