{"url":"/dataset/wdc-products-1","name":"WDC Products","full_name":null,"description_markdown":"**WDC Products** is an entity matching benchmark which provides for the systematic evaluation of matching systems along combinations of three dimensions while relying on real-word data. The three dimensions are \r\n\r\ni) amount of corner-cases \r\n\r\nii) generalization to unseen entities, and \r\n\r\niii) development set size\r\n\r\nWDC Products contains 11715 product offers describing in total 2162 product entities belonging to various product categories.\r\n\r\nSource: [WDC Products: A Multi-Dimensional Entity Matching Benchmark](https://arxiv.org/pdf/2301.09521v1.pdf)","description_withheld":null,"homepage":"http://webdatacommons.org/largescaleproductcorpus/wdc-products/","introduced_date":"2023-01-23","introduced_date_note":null,"introduced_by":{"paper":"/paper/wdc-products-a-multi-dimensional-entity","title":"WDC Products: A Multi-Dimensional Entity Matching Benchmark","first_author":"Ralph Peeters","url":null},"license":{"name":"BSD-3-Clause license","url":"https://github.com/wbsg-uni-mannheim/wdcproducts/blob/main/LICENSE"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Data Integration","url":"/task/data-integration","datasets_with_task":"/datasets/task/data-integration"},{"name":"Entity Resolution","url":"/task/entity-resolution","datasets_with_task":"/datasets/task/entity-resolution"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["WDC Products","WDC Products-80%cc-seen-medium","WDC Products-50%cc-unseen-medium","WDC Products-80%cc-seen-medium-multi"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/entity-resolution-on-wdc-products-80-cc-seen","task":"Entity Resolution","dataset_variant":"WDC Products-80%cc-seen-medium","rows":13,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"gpt4-0613_zeroshot","paper":"/paper/entity-matching-using-large-language-models","metrics":{"F1 (%)":"89.61"},"code_links":[{"title":"wbsg-uni-mannheim/matchgpt","url":"https://github.com/wbsg-uni-mannheim/matchgpt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/entity-resolution-on-wdc-products-50-cc","task":"Entity Resolution","dataset_variant":"WDC Products-50%cc-unseen-medium","rows":4,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"RoBERTa-base","paper":"/paper/wdc-products-a-multi-dimensional-entity","metrics":{"F1 (%)":"71.14"},"code_links":[{"title":"wbsg-uni-mannheim/wdcproducts","url":"https://github.com/wbsg-uni-mannheim/wdcproducts"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/entity-resolution-on-wdc-products-80-cc-seen-1","task":"Entity Resolution","dataset_variant":"WDC Products-80%cc-seen-medium-multi","rows":2,"metrics":["F1 Micro"],"first_row_in_archive_order":{"model":"RoBERTa-SupCon","paper":"/paper/wdc-products-a-multi-dimensional-entity","metrics":{"F1 Micro":"88.63"},"code_links":[{"title":"wbsg-uni-mannheim/wdcproducts","url":"https://github.com/wbsg-uni-mannheim/wdcproducts"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/entity-resolution-on-wdc-products","task":"Entity Resolution","dataset_variant":"WDC Products","rows":1,"metrics":["F1 (%)"],"first_row_in_archive_order":{"model":"gpt-4o-2024-08-06_fine_tuned_wdc_small","paper":"/paper/fine-tuning-large-language-models-for-entity","metrics":{"F1 (%)":"87.07"},"code_links":[{"title":"wbsg-uni-mannheim/tailormatch","url":"https://github.com/wbsg-uni-mannheim/tailormatch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fine-tuning-large-language-models-for-entity","title":"Fine-tuning Large Language Models for Entity Matching","date":"2024-09-12","rows_on_this_dataset":9,"code_links":1,"syntology":null},{"paper":"/paper/entity-matching-using-large-language-models","title":"Entity Matching using Large Language Models","date":"2023-10-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/wdc-products-a-multi-dimensional-entity","title":"WDC Products: A Multi-Dimensional Entity Matching Benchmark","date":"2023-01-23","rows_on_this_dataset":10,"code_links":1,"syntology":null}],"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."}