{"url":"/dataset/docred","name":"DocRED","full_name":null,"description_markdown":"**DocRED** (Document-Level Relation Extraction Dataset) is a relation extraction dataset constructed from Wikipedia and Wikidata. Each document in the dataset is human-annotated with named entity mentions, coreference information, intra- and inter-sentence relations, and supporting evidence. DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document. Along with the human-annotated data, the dataset provides large-scale distantly supervised data.\r\n\r\nDocRED contains 132,375 entities and 56,354 relational facts annotated on 5,053 Wikipedia documents. In addition to the human-annotated data, the dataset provides large-scale distantly supervised data over 101,873 documents.\r\n\r\nSource: [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://paperswithcode.com/paper/docred-a-large-scale-document-level-relation/)\r\nImage Source: [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://paperswithcode.com/paper/docred-a-large-scale-document-level-relation/)","description_withheld":null,"homepage":"https://github.com/thunlp/DocRED","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/docred-a-large-scale-document-level-relation","title":"DocRED: A Large-Scale Document-Level Relation Extraction Dataset","first_author":"Yuan Yao","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"Joint Entity and Relation Extraction","url":"/task/joint-entity-and-relation-extraction","datasets_with_task":"/datasets/task/joint-entity-and-relation-extraction"},{"name":"Few-Shot Relation Classification","url":"/task/few-shot-relation-classification","datasets_with_task":"/datasets/task/few-shot-relation-classification"},{"name":"Document-level Closed Information Extraction","url":"/task/document-level-closed-information-extraction","datasets_with_task":"/datasets/task/document-level-closed-information-extraction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["DocRED"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/thunlp/docred","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/docred","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/thunlp/DocRED","url":"https://github.com/thunlp/DocRED","frameworks":["pytorch"]}],"num_papers_in_archive":155,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/relation-extraction-on-docred","task":"Relation Extraction","dataset_variant":"DocRED","rows":62,"metrics":["F1","Ign 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F1"],"first_row_in_archive_order":{"model":"REXEL","paper":"/paper/rexel-an-end-to-end-model-for-document-level","metrics":{"Relation F1":"27.96"},"code_links":[{"title":"amazon-science/e2e-docie","url":"https://github.com/amazon-science/e2e-docie"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/few-shot-relation-classification-on-docred","task":"Few-Shot Relation Classification","dataset_variant":"DocRED","rows":1,"metrics":["F1 (1-Doc)","F1 (3-Doc)"],"first_row_in_archive_order":{"model":"DL-MNAV","paper":"/paper/few-shot-document-level-relation-extraction","metrics":{"F1 (1-Doc)":"7.05","F1 (3-Doc)":"8.42"},"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"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rexel-an-end-to-end-model-for-document-level","title":"REXEL: An End-to-end Model for Document-Level 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Learning","date":"2021-02-11","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/document-level-relation-extraction-with-1","title":"Document-Level Relation Extraction with Reconstruction","date":"2020-12-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/coarse-to-fine-entity-representations-for","title":"Coarse-to-Fine Entity Representations for Document-level Relation Extraction","date":"2020-12-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/dual-supervision-framework-for-relation","title":"Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation","date":"2020-11-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/document-level-relation-extraction-with","title":"Document-Level Relation Extraction with Adaptive Thresholding and Localized Context 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Representation","date":"2020-04-15","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/hin-hierarchical-inference-network-for","title":"HIN: Hierarchical Inference Network for Document-Level Relation Extraction","date":"2020-03-28","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/greg-a-global-level-relation-extraction-with","title":"GREG: A Global Level Relation Extraction with Knowledge Graph Embedding","date":"2020-02-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fine-tune-bert-for-docred-with-two-step","title":"Fine-tune Bert for DocRED with Two-step Process","date":"2019-09-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/docred-a-large-scale-document-level-relation","title":"DocRED: A Large-Scale Document-Level Relation Extraction 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