{"url":"/dataset/fewrel","name":"FewRel","full_name":"Few-Shot Relation Classification Dataset","description_markdown":"The **FewRel** (**Few-Shot Relation Classification Dataset**) contains 100 relations and 70,000 instances from Wikipedia. The dataset is divided into three subsets: training set (64 relations), validation set (16 relations) and test set (20 relations).\r\n\r\nSource: [Neural Snowball for Few-Shot Relation Learning](https://arxiv.org/abs/1908.11007)\r\nImage Source: [https://www.aclweb.org/anthology/D18-1514.pdf](https://www.aclweb.org/anthology/D18-1514.pdf)","description_withheld":null,"homepage":"http://www.zhuhao.me/fewrel/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/fewrel-a-large-scale-supervised-few-shot","title":"FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation","first_author":"Xu Han","url":null},"license":{"name":"CC BY-SA 4.0","url":"http://creativecommons.org/licenses/by-sa/4.0/legalcode"},"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"},{"name":"Few-Shot Relation Classification","url":"/task/few-shot-relation-classification","datasets_with_task":"/datasets/task/few-shot-relation-classification"},{"name":"Zero-shot Relation Triplet Extraction","url":"/task/zero-shot-relation-triplet-extraction","datasets_with_task":"/datasets/task/zero-shot-relation-triplet-extraction"},{"name":"Zero-shot Relation Classification","url":"/task/zero-shot-relation-classification","datasets_with_task":"/datasets/task/zero-shot-relation-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["FewRel"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/thunlp/few_rel","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/few_rel","frameworks":["tf","pytorch","jax"]}],"num_papers_in_archive":189,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/relation-classification-on-fewrel-1","task":"Relation Classification","dataset_variant":"FewRel","rows":5,"metrics":["F1 (10-way 1-shot)","F1 (10-way 5-shot)","F1 (5-way 1-shot)","F1 (5-way 5-shot","F1"],"first_row_in_archive_order":{"model":"DeepStruct multi-task w/ finetune","paper":"/paper/deepstruct-pretraining-of-language-models-for-1","metrics":{"F1 (10-way 1-shot)":"97.8","F1 (10-way 5-shot)":"99.8","F1 (5-way 1-shot)":"98.4","F1 (5-way 5-shot":"100"},"code_links":[{"title":"cgraywang/deepstruct","url":"https://github.com/cgraywang/deepstruct"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/zero-shot-relation-triplet-extraction-on","task":"Zero-shot Relation Triplet Extraction","dataset_variant":"FewRel","rows":3,"metrics":["Avg. F1"],"first_row_in_archive_order":{"model":"ZETT","paper":"/paper/zero-shot-triplet-extraction-by-template","metrics":{"Avg. F1":"31.28"},"code_links":[{"title":"megagonlabs/zett","url":"https://github.com/megagonlabs/zett"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/relation-extraction-on-fewrel","task":"Relation Extraction","dataset_variant":"FewRel","rows":2,"metrics":["F1","Precision","Recall"],"first_row_in_archive_order":{"model":"RoCORE","paper":"/paper/a-relation-oriented-clustering-method-for","metrics":{"F1":"79.611"},"code_links":[{"title":"ac-zyx/rocore","url":"https://github.com/ac-zyx/rocore"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/zero-shot-triplet-extraction-by-template","title":"Zero-shot Triplet Extraction by Template Infilling","date":"2022-12-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deepstruct-pretraining-of-language-models-for-1","title":"DeepStruct: Pretraining of Language Models for Structure Prediction","date":"2022-05-21","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":7,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/relationprompt-leveraging-prompts-to-generate","title":"RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction","date":"2022-03-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":26,"samples_ran":15,"samples_unverified":11,"pointer_only_for_licence":16,"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/a-relation-oriented-clustering-method-for","title":"A Relation-Oriented Clustering Method for Open Relation Extraction","date":"2021-09-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/two-are-better-than-one-joint-entity-and","title":"Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders","date":"2020-10-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":3,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":46,"samples_ran":25,"samples_unverified":21,"pointer_only_for_licence":21,"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."}