{"url":"/dataset/scierc","name":"SciERC","full_name":null,"description_markdown":"**SciERC** dataset is a collection of 500 scientific abstract annotated with scientific entities, their relations, and coreference clusters. The abstracts are taken from 12 AI conference/workshop proceedings in four AI communities, from the Semantic Scholar Corpus. SciERC extends previous datasets in scientific articles SemEval 2017 Task 10 and SemEval 2018 Task 7 by extending entity types, relation types, relation coverage, and adding cross-sentence relations using coreference links.\r\n\r\nSource: [http://nlp.cs.washington.edu/sciIE/](http://nlp.cs.washington.edu/sciIE/)\r\nImage Source: [http://nlp.cs.washington.edu/sciIE/](http://nlp.cs.washington.edu/sciIE/)","description_withheld":null,"homepage":"http://nlp.cs.washington.edu/sciIE/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/multi-task-identification-of-entities","title":"Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction","first_author":"Yi Luan","url":null},"license":{"name":"Unknown","url":null},"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":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"UIE","url":"/task/uie","datasets_with_task":"/datasets/task/uie"},{"name":"Joint Entity and Relation Extraction","url":"/task/joint-entity-and-relation-extraction","datasets_with_task":"/datasets/task/joint-entity-and-relation-extraction"},{"name":"Named Entity Recognition","url":"/task/named-entity-recognition-1","datasets_with_task":"/datasets/task/named-entity-recognition-1"},{"name":"Few-Shot Relation Classification","url":"/task/few-shot-relation-classification","datasets_with_task":"/datasets/task/few-shot-relation-classification"},{"name":"Continual Pretraining","url":"/task/continual-pretraining","datasets_with_task":"/datasets/task/continual-pretraining"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["SciERC","sciERC-sent"],"data_loaders":[],"num_papers_in_archive":134,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/joint-entity-and-relation-extraction-on","task":"Joint Entity and Relation Extraction","dataset_variant":"SciERC","rows":11,"metrics":["Relation F1","Entity F1","RE+ Micro F1","Cross Sentence"],"first_row_in_archive_order":{"model":"PL-Marker","paper":"/paper/pack-together-entity-and-relation-extraction","metrics":{"Cross Sentence":"Yes","Entity F1":"69.9","RE+ Micro F1":"41.6","Relation F1":"53.2"},"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"},{"leaderboard":"/sota/named-entity-recognition-ner-on-scierc","task":"Named Entity Recognition (NER)","dataset_variant":"SciERC","rows":7,"metrics":["F1"],"first_row_in_archive_order":{"model":"SciDeBERTa v2","paper":"/paper/scideberta-learning-deberta-for-science","metrics":{"F1":"72.4"},"code_links":[{"title":"Eunhui-Kim/SciDeBERTa-Fine-Tuning","url":"https://github.com/Eunhui-Kim/SciDeBERTa-Fine-Tuning"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/relation-extraction-on-scierc","task":"Relation Extraction","dataset_variant":"SciERC","rows":3,"metrics":["F1","NER Micro F1","RE+ Micro F1"],"first_row_in_archive_order":{"model":"SciBERT (SciVocab)","paper":"/paper/scibert-pretrained-contextualized-embeddings","metrics":{"F1":"74.64"},"code_links":[{"title":"allenai/scibert","url":"https://github.com/allenai/scibert"},{"title":"charles9n/bert-sklearn","url":"https://github.com/charles9n/bert-sklearn"},{"title":"tetsu9923/scireviewgen","url":"https://github.com/tetsu9923/scireviewgen"},{"title":"georgetown-cset/ai-relevant-papers","url":"https://github.com/georgetown-cset/ai-relevant-papers"},{"title":"kuldeep7688/BioMedicalBertNer","url":"https://github.com/kuldeep7688/BioMedicalBertNer"},{"title":"hoangcuongnguyen2001/scibert-for-technique-classification","url":"https://github.com/hoangcuongnguyen2001/scibert-for-technique-classification"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/continual-pretraining-on-scierc","task":"Continual Pretraining","dataset_variant":"SciERC","rows":1,"metrics":["F1 (macro)"],"first_row_in_archive_order":{"model":"DAS","paper":"/paper/continual-learning-of-language-models","metrics":{"F1 (macro)":"0.7093"},"code_links":[{"title":"zixuanke/pycontinual","url":"https://github.com/zixuanke/pycontinual"},{"title":"UIC-Liu-Lab/ContinualLM","url":"https://github.com/UIC-Liu-Lab/ContinualLM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/few-shot-relation-classification-on-scierc","task":"Few-Shot Relation Classification","dataset_variant":"SciERC","rows":1,"metrics":["F1 (1-Doc)","F1 (3-Doc)"],"first_row_in_archive_order":{"model":"DL-MNAV+SIE+SBN","paper":"/paper/few-shot-document-level-relation-extraction","metrics":{"F1 (1-Doc)":"2.85","F1 (3-Doc)":"3.72"},"code_links":[{"title":"nicpopovic/fredo","url":"https://github.com/nicpopovic/fredo"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/relation-extraction-on-scierc-sent","task":"Relation Extraction","dataset_variant":"sciERC-sent","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"RELA","paper":"/paper/sequence-generation-with-label-augmentation","metrics":{"F1":"90.3"},"code_links":[{"title":"pkuserc/rela","url":"https://github.com/pkuserc/rela"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/uie-on-scierc","task":"UIE","dataset_variant":"SciERC","rows":1,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"KnowCoder-7b-IE","paper":"/paper/knowcoder-coding-structured-knowledge-into","metrics":{"F1 score":"40.0"},"code_links":[{"title":"ICT-GoKnow/KnowCoder","url":"https://github.com/ICT-GoKnow/KnowCoder"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/knowcoder-coding-structured-knowledge-into","title":"KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction","date":"2024-03-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/continual-learning-of-language-models","title":"Continual Pre-training of Language Models","date":"2023-02-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/sequence-generation-with-label-augmentation","title":"Sequence Generation with Label Augmentation for Relation Extraction","date":"2022-12-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-multi-gate-encoder-for-joint-entity-and","title":"A Multi-Gate Encoder for Joint Entity and Relation Extraction","date":"2022-10-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/scideberta-learning-deberta-for-science","title":"SciDeBERTa: Learning DeBERTa for Science Technology Documents and Fine-Tuning Information Extraction Tasks","date":"2022-06-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/few-shot-document-level-relation-extraction","title":"Few-Shot Document-Level Relation Extraction","date":"2022-05-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/joint-entity-and-relation-extraction-from","title":"Joint Entity and Relation Extraction from Scientific Documents: Role of Linguistic Information and Entity Types","date":"2021-09-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/a-partition-filter-network-for-joint-entity","title":"A Partition Filter Network for Joint Entity and Relation Extraction","date":"2021-08-27","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":5,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-trigger-sense-memory-flow-framework-for","title":"A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction","date":"2021-01-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-robust-and-domain-adaptive-approach-for-low","title":"A Robust and Domain-Adaptive Approach for Low-Resource Named Entity Recognition","date":"2021-01-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-frustratingly-easy-approach-for-joint","title":"A Frustratingly Easy Approach for Entity and Relation Extraction","date":"2020-10-24","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/span-based-joint-entity-and-relation","title":"Span-based Joint Entity and Relation Extraction with Transformer Pre-training","date":"2019-09-17","rows_on_this_dataset":3,"code_links":3,"syntology":null},{"paper":"/paper/entity-relation-and-event-extraction-with","title":"Entity, Relation, and Event Extraction with Contextualized Span Representations","date":"2019-09-08","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-general-framework-for-information","title":"A General Framework for Information Extraction using Dynamic Span Graphs","date":"2019-04-05","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/scibert-pretrained-contextualized-embeddings","title":"SciBERT: A Pretrained Language Model for Scientific Text","date":"2019-03-26","rows_on_this_dataset":4,"code_links":6,"syntology":null},{"paper":"/paper/multi-task-identification-of-entities","title":"Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction","date":"2018-08-29","rows_on_this_dataset":2,"code_links":5,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":27,"samples_ran":11,"samples_unverified":16,"pointer_only_for_licence":2,"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."}