{"id":13754353,"url":"https://github.com/thunlp/DocRED","last_synced_at":"2025-05-09T22:31:59.503Z","repository":{"id":45717030,"uuid":"189986386","full_name":"thunlp/DocRED","owner":"thunlp","description":"Dataset and codes for ACL 2019 DocRED: A Large-Scale Document-Level Relation Extraction Dataset.","archived":false,"fork":false,"pushed_at":"2020-12-01T03:18:37.000Z","size":57,"stargazers_count":637,"open_issues_count":11,"forks_count":110,"subscribers_count":17,"default_branch":"master","last_synced_at":"2025-04-05T01:05:30.257Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thunlp.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-06-03T10:43:17.000Z","updated_at":"2025-04-04T10:23:53.000Z","dependencies_parsed_at":"2022-08-12T12:10:08.988Z","dependency_job_id":null,"html_url":"https://github.com/thunlp/DocRED","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thunlp%2FDocRED","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thunlp%2FDocRED/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thunlp%2FDocRED/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thunlp%2FDocRED/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thunlp","download_url":"https://codeload.github.com/thunlp/DocRED/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253335827,"owners_count":21892745,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-03T09:01:56.448Z","updated_at":"2025-05-09T22:31:54.481Z","avatar_url":"https://github.com/thunlp.png","language":"Python","funding_links":[],"categories":["关系抽取、信息抽取","Tasks and Methods"],"sub_categories":["其他_文本生成、文本对话","Information Extraction Beyond NER"],"readme":"# DocRED\nDataset and code for baselines for [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://arxiv.org/abs/1906.06127v3)\n\nMultiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, a new dataset constructed from Wikipedia and Wikidata with three features: \n\n+ DocRED annotates both named entities and relations, and is the largest human-annotated dataset for document-level RE from plain text.\n+ DocRED requires reading multiple sentences in a document to extract entities and infer their relations by synthesizing all information of the document.\n+ Along with the human-annotated data, we also offer large-scale distantly supervised data, which enables DocRED to be adopted for both supervised and weakly supervised scenarios.\n\n## Codalab\nIf you are interested in our dataset, you are welcome to join in the Codalab competition at [DocRED](https://competitions.codalab.org/competitions/20717)\n\n\n## Cite\nIf you use the dataset or the code, please cite this paper:\n```\n@inproceedings{yao2019DocRED,\n  title={{DocRED}: A Large-Scale Document-Level Relation Extraction Dataset},\n  author={Yao, Yuan and Ye, Deming and Li, Peng and Han, Xu and Lin, Yankai and Liu, Zhenghao and Liu, Zhiyuan and Huang, Lixin and Zhou, Jie and Sun, Maosong},\n  booktitle={Proceedings of ACL 2019},\n  year={2019}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthunlp%2FDocRED","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthunlp%2FDocRED","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthunlp%2FDocRED/lists"}