{"url":"/dataset/open-entity-1","name":"Open Entity","full_name":null,"description_markdown":"The **Open Entity** dataset is a collection of about 6,000 sentences with fine-grained entity types annotations. The entity types are free-form noun phrases that describe appropriate types for the role the target entity plays in the sentence. Sentences were sampled from Gigaword, OntoNotes and web articles. On average each sentence has 5 labels.\r\n\r\nSource: [Ultra-Fine Entity Typing](https://paperswithcode.com/paper/ultra-fine-entity-typing/)\r\nImage Source: [Ultra-Fine Entity Typing](https://paperswithcode.com/paper/ultra-fine-entity-typing/)","description_withheld":null,"homepage":"https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/ultra-fine-entity-typing","title":"Ultra-Fine Entity Typing","first_author":"Eunsol Choi","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Entity Typing","url":"/task/entity-typing","datasets_with_task":"/datasets/task/entity-typing"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":[" Open Entity","Open Entity"],"data_loaders":[],"num_papers_in_archive":35,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/entity-typing-on-open-entity-1","task":"Entity Typing","dataset_variant":"Open Entity","rows":13,"metrics":["F1"],"first_row_in_archive_order":{"model":"MLMET","paper":"/paper/luke-deep-contextualized-entity","metrics":{"F1":"78.2"},"code_links":[{"title":"huggingface/transformers","url":"https://github.com/huggingface/transformers"},{"title":"PaddlePaddle/PaddleNLP","url":"https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/roformer"},{"title":"studio-ousia/luke","url":"https://github.com/studio-ousia/luke"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/nlp/luke"},{"title":"JiachengLi1995/UCTopic","url":"https://github.com/JiachengLi1995/UCTopic"},{"title":"shmulvad/zero-for-ner","url":"https://github.com/shmulvad/zero-for-ner"},{"title":"Beacontownfc/paddle_luke_stable","url":"https://github.com/Beacontownfc/paddle_luke_stable"},{"title":"pwc-1/Paper-9","url":"https://github.com/pwc-1/Paper-9/tree/main/luke"},{"title":"2023-MindSpore-1/ms-code-156","url":"https://github.com/2023-MindSpore-1/ms-code-156"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/entity-typing-on-open-entity","task":"Entity Typing","dataset_variant":"Open Entity","rows":3,"metrics":["F1","Precision","Recall"],"first_row_in_archive_order":{"model":"K-Adapter ( fac-adapter )","paper":"/paper/k-adapter-infusing-knowledge-into-pre-trained","metrics":{"F1":"77.6916","Precision":"79.6712","Recall":"75.8081"},"code_links":[{"title":"microsoft/K-Adapter","url":"https://github.com/microsoft/K-Adapter"},{"title":"stevekgyang/sccl","url":"https://github.com/stevekgyang/sccl"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/recall-expand-and-multi-candidate-cross","title":"Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing","date":"2022-12-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/modeling-label-correlations-for-ultra-fine","title":"Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field","date":"2022-12-03","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/unified-semantic-typing-with-meaningful-label","title":"Unified Semantic Typing with Meaningful Label Inference","date":"2022-05-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fine-grained-entity-typing-via-label","title":"Fine-grained Entity Typing via Label Reasoning","date":"2021-09-13","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/ultra-fine-entity-typing-with-weak","title":"Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model","date":"2021-06-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/modeling-fine-grained-entity-types-with-box","title":"Modeling Fine-Grained Entity Types with Box Embeddings","date":"2021-01-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/luke-deep-contextualized-entity","title":"LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention","date":"2020-10-02","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/k-adapter-infusing-knowledge-into-pre-trained","title":"K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters","date":"2020-02-05","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":7,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ernie-enhanced-language-representation-with","title":"ERNIE: Enhanced Language Representation with Informative Entities","date":"2019-05-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-to-denoise-distantly-labeled-data","title":"Learning to Denoise Distantly-Labeled Data for Entity Typing","date":"2019-05-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/imposing-label-relational-inductive-bias-for","title":"Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing","date":"2019-03-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ultra-fine-entity-typing","title":"Ultra-Fine Entity Typing","date":"2018-07-13","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":28,"samples_ran":14,"samples_unverified":14,"pointer_only_for_licence":1,"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."}