{"url":"/dataset/tacred","name":"TACRED","full_name":"The TAC Relation Extraction Dataset","description_markdown":"TACRED is a large-scale relation extraction dataset with 106,264 examples built over newswire and web text from the corpus used in the yearly TAC Knowledge Base Population (TAC KBP) challenges. Examples in TACRED cover 41 relation types as used in the TAC KBP challenges (e.g., per:schools_attended and org:members) or are labeled as no_relation if no defined relation is held. These examples are created by combining available human annotations from the TAC KBP challenges and crowdsourcing.\r\n\r\nSource: https://nlp.stanford.edu/projects/tacred/","description_withheld":null,"homepage":"https://nlp.stanford.edu/projects/tacred/","introduced_date":"2017-09-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/position-aware-attention-and-supervised-data","title":"Position-aware Attention and Supervised Data Improve Slot Filling","first_author":"Yuhao Zhang","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Relation Extraction","url":"/task/relation-extraction","datasets_with_task":"/datasets/task/relation-extraction"},{"name":"Relation Classification","url":"/task/relation-classification","datasets_with_task":"/datasets/task/relation-classification"}],"languages":[],"variants":["TACRED"],"data_loaders":[],"num_papers_in_archive":204,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/relation-extraction-on-tacred","task":"Relation Extraction","dataset_variant":"TACRED","rows":40,"metrics":["F1","F1 (10% Few-Shot)","F1 (5% Few-Shot)","F1 (1% Few-Shot)","F1 (Zero-Shot)"],"first_row_in_archive_order":{"model":"RAG4RE","paper":"/paper/retrieval-augmented-generation-based-relation","metrics":{"F1":"86.6"},"code_links":[{"title":"sefeoglu/rag4re","url":"https://github.com/sefeoglu/rag4re"}]},"note":"rows are the archive's own order at snapshot; 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not a correctness claim."}},{"paper":"/paper/summarization-as-indirect-supervision-for","title":"Summarization as Indirect Supervision for Relation Extraction","date":"2022-05-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/zero-shot-information-extraction-as-a-unified","title":"Zero-Shot Information Extraction as a Unified Text-to-Triple Translation","date":"2021-09-23","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/label-verbalization-and-entailment-for","title":"Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction","date":"2021-09-08","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/improving-sentence-level-relation-extraction","title":"Improving Sentence-Level Relation Extraction through Curriculum Learning","date":"2021-07-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/relation-classification-with-entity-type","title":"Relation Classification with Entity Type Restriction","date":"2021-05-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-from-noisy-labels-for-entity-centric","title":"Learning from Noisy Labels for Entity-Centric Information Extraction","date":"2021-04-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/an-improved-baseline-for-sentence-level","title":"An Improved Baseline for Sentence-level Relation Extraction","date":"2021-02-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/structured-prediction-as-translation-between-1","title":"Structured Prediction as Translation between Augmented Natural Languages","date":"2021-01-14","rows_on_this_dataset":3,"code_links":2,"syntology":null},{"paper":"/paper/gdpnet-refining-latent-multi-view-graph-for","title":"GDPNet: Refining Latent Multi-View Graph for Relation Extraction","date":"2020-12-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/relation-extraction-as-two-way-span","title":"Relation Classification as Two-way Span-Prediction","date":"2020-10-09","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-from-context-or-names-an-empirical","title":"Learning from Context or Names? An Empirical Study on Neural Relation Extraction","date":"2020-10-05","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":2,"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/denert-kg-named-entity-and-relation","title":"DeNERT-KG: Named Entity and Relation Extraction Model Using DQN, Knowledge Graph, and BERT","date":"2020-09-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/efficient-long-distance-relation-extraction","title":"Efficient long-distance relation extraction with DG-SpanBERT","date":"2020-04-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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":3,"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/kepler-a-unified-model-for-knowledge","title":"KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation","date":"2019-11-13","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":1,"samples_unverified":4,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/knowledge-enhanced-contextual-word","title":"Knowledge Enhanced Contextual Word Representations","date":"2019-09-09","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/beyond-word-attention-using-segment-attention","title":"Beyond Word Attention: Using Segment Attention in Neural Relation Extraction","date":"2019-08-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spanbert-improving-pre-training-by","title":"SpanBERT: Improving Pre-training by Representing and Predicting Spans","date":"2019-07-24","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":3,"samples_unverified":12,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/attention-guided-graph-convolutional-networks","title":"Attention Guided Graph Convolutional Networks for Relation Extraction","date":"2019-06-18","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":1,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/matching-the-blanks-distributional-similarity","title":"Matching the Blanks: Distributional Similarity for Relation Learning","date":"2019-06-07","rows_on_this_dataset":2,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":2,"samples_unverified":12,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/improving-relation-extraction-by-pre-trained-1","title":"Improving Relation Extraction by Pre-trained Language Representations","date":"2019-06-07","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/enriching-pre-trained-language-model-with","title":"Enriching Pre-trained Language Model with Entity Information for Relation Classification","date":"2019-05-20","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"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":3,"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/simple-bert-models-for-relation-extraction","title":"Simple BERT Models for Relation Extraction and Semantic Role Labeling","date":"2019-04-10","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/simplifying-graph-convolutional-networks","title":"Simplifying Graph Convolutional Networks","date":"2019-02-19","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-convolution-over-pruned-dependency","title":"Graph Convolution over Pruned Dependency Trees Improves Relation Extraction","date":"2018-09-26","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/position-aware-attention-and-supervised-data","title":"Position-aware Attention and Supervised Data Improve Slot Filling","date":"2017-09-01","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":13,"samples_harvested":109,"samples_ran":34,"samples_unverified":75,"pointer_only_for_licence":11,"papers_with_no_sample_that_ran":2,"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."}