{"url":"/dataset/few-nerd","name":"Few-NERD","full_name":"Few-NERD","description_markdown":"Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities, and 4,601,223 tokens. Three benchmark tasks are built, one is supervised (Few-NERD (SUP)) and the other two are few-shot (Few-NERD (INTRA) and Few-NERD (INTER)).","description_withheld":null,"homepage":"https://ningding97.github.io/fewnerd/","introduced_date":"2021-05-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/few-nerd-a-few-shot-named-entity-recognition","title":"Few-NERD: A Few-Shot Named Entity Recognition Dataset","first_author":"Ning Ding","url":null},"license":{"name":"CC BY-SA 4.0","url":"http://creativecommons.org/licenses/by-sa/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"},{"name":"Named Entity Recognition","url":"/task/named-entity-recognition-1","datasets_with_task":"/datasets/task/named-entity-recognition-1"},{"name":"Entity Typing","url":"/task/entity-typing","datasets_with_task":"/datasets/task/entity-typing"},{"name":"Few-shot NER","url":"/task/few-shot-ner","datasets_with_task":"/datasets/task/few-shot-ner"},{"name":"Low Resource Named Entity Recognition","url":"/task/low-resource-named-entity-recognition","datasets_with_task":"/datasets/task/low-resource-named-entity-recognition"},{"name":"Multi-Grained Named Entity Recognition","url":"/task/multi-grained-named-entity-recognition","datasets_with_task":"/datasets/task/multi-grained-named-entity-recognition"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Few-NERD","Few-NERD (SUP)","Few-NERD (INTRA)","Few-NERD (INTER)","finegrained, supervised FewNERD","FewNERD"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/DFKI-SLT/few-nerd","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/dfki-nlp/few-nerd","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":77,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/few-shot-ner-on-few-nerd-inter","task":"Few-shot NER","dataset_variant":"Few-NERD (INTER)","rows":13,"metrics":["5 way 1~2 shot","5 way 5~10 shot","10 way 1~2 shot","10 way 5~10 shot","Average"],"first_row_in_archive_order":{"model":"MSDP","paper":"/paper/a-multi-task-semantic-decomposition-framework","metrics":{"10 way 1~2 shot":"69.78±0.31","10 way 5~10 shot":"81.50±0.71","5 way 1~2 shot":"76.86±0.22","5 way 5~10 shot":"84.78±0.69"},"code_links":[{"title":"dongguanting/msdp-fewshot-ner","url":"https://github.com/dongguanting/msdp-fewshot-ner"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/few-shot-ner-on-few-nerd-intra","task":"Few-shot NER","dataset_variant":"Few-NERD (INTRA)","rows":13,"metrics":["5 way 1~2 shot","5 way 5~10 shot","10 way 1~2 shot","10 way 5~10 shot","Average"],"first_row_in_archive_order":{"model":"JCELRNER","paper":null,"metrics":{"5 way 1~2 shot":"62.49±0.43","5 way 5~10 shot":"71.62±0.44"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/named-entity-recognition-on-few-nerd-sup","task":"Named Entity Recognition (NER)","dataset_variant":"Few-NERD (SUP)","rows":6,"metrics":["F1-Measure","Precision","Recall"],"first_row_in_archive_order":{"model":"PL-Marker","paper":"/paper/pack-together-entity-and-relation-extraction","metrics":{"F1-Measure":"70.9","Precision":"71.2","Recall":"70.6"},"code_links":[{"title":"tomaarsen/spanmarkerner","url":"https://github.com/tomaarsen/spanmarkerner"},{"title":"thunlp/pl-marker","url":"https://github.com/thunlp/pl-marker"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/decomposed-meta-learning-for-few-shot","title":"Decomposed Meta-Learning for Few-Shot Sequence Labeling","date":"2024-03-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/nuner-entity-recognition-encoder-pre-training","title":"NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data","date":"2024-02-23","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/heproto-a-hierarchical-enhancing-protonet","title":"HEProto: A Hierarchical Enhancing ProtoNet based on Multi-Task Learning for Few-shot Named Entity Recognition","date":"2023-10-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/a-multi-task-semantic-decomposition-framework","title":"A Multi-Task Semantic Decomposition Framework with Task-specific Pre-training for Few-Shot NER","date":"2023-08-28","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/type-aware-decomposed-framework-for-few-shot","title":"Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition","date":"2023-02-13","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/language-model-pre-training-with-sparse","title":"Language Model Pre-Training with Sparse Latent Typing","date":"2022-10-23","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":4,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/decomposed-meta-learning-for-few-shot-named","title":"Decomposed Meta-Learning for Few-Shot Named Entity Recognition","date":"2022-04-12","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/parallel-instance-query-network-for-named","title":"Parallel Instance Query Network for Named Entity Recognition","date":"2022-03-20","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/an-enhanced-span-based-decomposition-method","title":"An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling","date":"2021-09-27","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/container-few-shot-named-entity-recognition","title":"CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning","date":"2021-09-15","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pack-together-entity-and-relation-extraction","title":"Packed Levitated Marker for Entity and Relation Extraction","date":"2021-09-13","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/few-nerd-a-few-shot-named-entity-recognition","title":"Few-NERD: A Few-Shot Named Entity Recognition Dataset","date":"2021-05-16","rows_on_this_dataset":7,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":1,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":38,"samples_ran":11,"samples_unverified":27,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"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."}