{"url":"/dataset/asnq","name":"ASNQ","full_name":"Answer Sentence Natural Questions","description_markdown":"A large scale dataset to enable the transfer step, exploiting the Natural Questions dataset. \r\n\r\nSource: [TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection](/paper/tanda-transfer-and-adapt-pre-trained)","description_withheld":null,"homepage":"https://github.com/alexa/wqa_tanda","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/tanda-transfer-and-adapt-pre-trained","title":"TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection","first_author":"Siddhant Garg","url":null},"license":null,"modalities":[],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Information Retrieval","url":"/task/information-retrieval","datasets_with_task":"/datasets/task/information-retrieval"},{"name":"Answer Selection","url":"/task/answer-selection","datasets_with_task":"/datasets/task/answer-selection"}],"languages":[],"variants":["ASNQ"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/AmazonScience/asnq","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/asnq","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/alexa/wqa_tanda","url":"https://github.com/alexa/wqa_tanda","frameworks":[]}],"num_papers_in_archive":25,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/answer-selection-on-asnq","task":"Answer Selection","dataset_variant":"ASNQ","rows":3,"metrics":["MAP","MRR"],"first_row_in_archive_order":{"model":"DeBERTa-V3-Large + SSP","paper":"/paper/pre-training-transformer-models-with-sentence","metrics":{"MAP":"0.743","MRR":"0.800"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/pre-training-transformer-models-with-sentence","title":"Pre-training Transformer Models with Sentence-Level Objectives for Answer Sentence Selection","date":"2022-05-20","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/paragraph-based-transformer-pre-training-for","title":"Paragraph-based Transformer Pre-training for Multi-Sentence Inference","date":"2022-05-02","rows_on_this_dataset":1,"code_links":1,"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."}