{"url":"/sota/attribute-value-extraction-on-mave","task":{"name":"Attribute Value Extraction","url":"/task/attribute-value-extraction","note":null},"dataset":{"name":"MAVE","url":"/dataset/mave"},"category":"Natural Language Processing","categories":["Natural Language Processing"],"category_note":null,"description":"**Attribute Value Extraction** is the task of extracting values for a given set of attributes of interest from free text input. Attribute value extraction is for example applied in the context of e-commerce where product attribute values are extracted from product offers.\r\n\r\nThe related task [Attribute Mining](https://paperswithcode.com/task/attribute-mining) assume that the target attribute set is unknown, while attribute value extraction assumes that the attribute set is given.\r\n[Multimodal Attribute Extraction](https://paperswithcode.com/task/multimodal-attribute-value-extraction) aims at extracting attribute values from multi-modal input such as text plus images.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["F1-score"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"F1-score":"higher"}},"counts":{"rows":3,"rows_with_code":3,"rows_with_paper_page":3,"rows_dated":3,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MAVEQA","metrics":{"F1-score":"98.32"},"uses_additional_data":false,"paper_date":"2021-12-16","paper":"/paper/mave-a-product-dataset-for-multi-source","paper_url":"https://arxiv.org/abs/2112.08663v1","paper_title":"MAVE: A Product Dataset for Multi-source Attribute Value Extraction","code":"https://github.com/google-research-datasets/mave","n_code_links":1,"syntology":null},{"rank_in_archive_order":2,"model":"AVEQA","metrics":{"F1-score":"98.14"},"uses_additional_data":false,"paper_date":"2021-12-16","paper":"/paper/mave-a-product-dataset-for-multi-source","paper_url":"https://arxiv.org/abs/2112.08663v1","paper_title":"MAVE: A Product Dataset for Multi-source Attribute Value Extraction","code":"https://github.com/google-research-datasets/mave","n_code_links":1,"syntology":null},{"rank_in_archive_order":3,"model":"AD-Opentag","metrics":{"F1-score":"79.73"},"uses_additional_data":false,"paper_date":"2021-12-16","paper":"/paper/mave-a-product-dataset-for-multi-source","paper_url":"https://arxiv.org/abs/2112.08663v1","paper_title":"MAVE: A Product Dataset for Multi-source Attribute Value Extraction","code":"https://github.com/google-research-datasets/mave","n_code_links":1,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}