{"url":"/dataset/torque","name":"Torque","full_name":null,"description_markdown":"Torque is an English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships.\r\n\r\nSource: [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](/paper/torque-a-reading-comprehension-dataset-of)","description_withheld":null,"homepage":"https://allennlp.org/torque.html","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/torque-a-reading-comprehension-dataset-of","title":"TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions","first_author":"Qiang Ning","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Natural Language Inference","url":"/task/natural-language-inference","datasets_with_task":"/datasets/task/natural-language-inference"},{"name":"Graph Generation","url":"/task/graph-generation","datasets_with_task":"/datasets/task/graph-generation"},{"name":"Abusive Language","url":"/task/abusive-language","datasets_with_task":"/datasets/task/abusive-language"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Torque"],"data_loaders":[],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/question-answering-on-torque","task":"Question Answering","dataset_variant":"Torque","rows":2,"metrics":["F1","EM","C"],"first_row_in_archive_order":{"model":"ECONET","paper":"/paper/deer-a-data-efficient-language-model-for","metrics":{"C":"37.0","EM":"52.0","F1":"76.3"},"code_links":[{"title":"ZHEvent/ZHEvent.github.io","url":"https://github.com/ZHEvent/ZHEvent.github.io"},{"title":"pluslabnlp/econet","url":"https://github.com/pluslabnlp/econet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deer-a-data-efficient-language-model-for","title":"ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning","date":"2020-12-30","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/torque-a-reading-comprehension-dataset-of","title":"TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions","date":"2020-05-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+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."}