{"url":"/dataset/multiconer","name":"MultiCoNER","full_name":null,"description_markdown":"**MultiCoNER** is a large multilingual dataset (11 languages) for Named Entity Recognition. It is designed to represent some of the contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities such as movie titles, and long-tail entity distributions.","description_withheld":null,"homepage":"https://registry.opendata.aws/multiconer/","introduced_date":"2022-08-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/multiconer-a-large-scale-multilingual-dataset","title":"MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition","first_author":"Shervin Malmasi","url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Named Entity Recognition (NER)","url":"/task/named-entity-recognition-ner","datasets_with_task":"/datasets/task/named-entity-recognition-ner"}],"languages":[],"variants":["MultiCoNER"],"data_loaders":[],"num_papers_in_archive":47,"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":"2026-09-24T18:15:14+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."}