{"url":"/dataset/fewclue","name":"FewCLUE","full_name":null,"description_markdown":"Chinese Few-shot Learning Evaluation Benchmark (FewCLUE) is a comprehensive small sample evaluation benchmark in Chinese. It includes nine tasks, ranging from single-sentence and sentence-pair classification tasks to machine reading comprehension tasks.","description_withheld":null,"homepage":"https://github.com/CLUEbenchmark/FewCLUE","introduced_date":"2021-07-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/fewclue-a-chinese-few-shot-learning","title":"FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark","first_author":"Liang Xu","url":null},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Language Modelling","url":"/task/language-modelling","datasets_with_task":"/datasets/task/language-modelling"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["FewCLUE","FewCLUE (EPRSTMT)","FewCLUE (OCNLI-FC)","FewCLUE (BUSTM)","FewCLUE (CHID-FC)","FewCLUE (CLUEWSC-FC)"],"data_loaders":[],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/language-modelling-on-fewclue-bustm","task":"Language Modelling","dataset_variant":"FewCLUE (BUSTM)","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GLM-130B","paper":"/paper/glm-130b-an-open-bilingual-pre-trained-model","metrics":{"Accuracy":"77.5"},"code_links":[{"title":"thudm/chatglm2-6b","url":"https://github.com/thudm/chatglm2-6b"},{"title":"thudm/chatglm3","url":"https://github.com/thudm/chatglm3"},{"title":"thudm/chatglm","url":"https://github.com/thudm/chatglm"},{"title":"modelscope/modelscope","url":"https://github.com/modelscope/modelscope"},{"title":"thudm/glm-130b","url":"https://github.com/thudm/glm-130b"},{"title":"THUDM/GLM","url":"https://github.com/THUDM/GLM"},{"title":"jackaduma/ChatGLM-LoRA-RLHF-PyTorch","url":"https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/language-modelling-on-fewclue-chid-fc","task":"Language Modelling","dataset_variant":"FewCLUE (CHID-FC)","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GLM-130B","paper":"/paper/glm-130b-an-open-bilingual-pre-trained-model","metrics":{"Accuracy":"90.1"},"code_links":[{"title":"thudm/chatglm2-6b","url":"https://github.com/thudm/chatglm2-6b"},{"title":"thudm/chatglm3","url":"https://github.com/thudm/chatglm3"},{"title":"thudm/chatglm","url":"https://github.com/thudm/chatglm"},{"title":"modelscope/modelscope","url":"https://github.com/modelscope/modelscope"},{"title":"thudm/glm-130b","url":"https://github.com/thudm/glm-130b"},{"title":"THUDM/GLM","url":"https://github.com/THUDM/GLM"},{"title":"jackaduma/ChatGLM-LoRA-RLHF-PyTorch","url":"https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/language-modelling-on-fewclue-cluewsc-fc","task":"Language Modelling","dataset_variant":"FewCLUE (CLUEWSC-FC)","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GLM-130B","paper":"/paper/glm-130b-an-open-bilingual-pre-trained-model","metrics":{"Accuracy":"77.4"},"code_links":[{"title":"thudm/chatglm2-6b","url":"https://github.com/thudm/chatglm2-6b"},{"title":"thudm/chatglm3","url":"https://github.com/thudm/chatglm3"},{"title":"thudm/chatglm","url":"https://github.com/thudm/chatglm"},{"title":"modelscope/modelscope","url":"https://github.com/modelscope/modelscope"},{"title":"thudm/glm-130b","url":"https://github.com/thudm/glm-130b"},{"title":"THUDM/GLM","url":"https://github.com/THUDM/GLM"},{"title":"jackaduma/ChatGLM-LoRA-RLHF-PyTorch","url":"https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/language-modelling-on-fewclue-eprstmt","task":"Language Modelling","dataset_variant":"FewCLUE (EPRSTMT)","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GLM-130B","paper":"/paper/glm-130b-an-open-bilingual-pre-trained-model","metrics":{"Accuracy":"92.5"},"code_links":[{"title":"thudm/chatglm2-6b","url":"https://github.com/thudm/chatglm2-6b"},{"title":"thudm/chatglm3","url":"https://github.com/thudm/chatglm3"},{"title":"thudm/chatglm","url":"https://github.com/thudm/chatglm"},{"title":"modelscope/modelscope","url":"https://github.com/modelscope/modelscope"},{"title":"thudm/glm-130b","url":"https://github.com/thudm/glm-130b"},{"title":"THUDM/GLM","url":"https://github.com/THUDM/GLM"},{"title":"jackaduma/ChatGLM-LoRA-RLHF-PyTorch","url":"https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/language-modelling-on-fewclue-ocnli-fc","task":"Language Modelling","dataset_variant":"FewCLUE (OCNLI-FC)","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"GLM-130B","paper":"/paper/glm-130b-an-open-bilingual-pre-trained-model","metrics":{"Accuracy":"73.8"},"code_links":[{"title":"thudm/chatglm2-6b","url":"https://github.com/thudm/chatglm2-6b"},{"title":"thudm/chatglm3","url":"https://github.com/thudm/chatglm3"},{"title":"thudm/chatglm","url":"https://github.com/thudm/chatglm"},{"title":"modelscope/modelscope","url":"https://github.com/modelscope/modelscope"},{"title":"thudm/glm-130b","url":"https://github.com/thudm/glm-130b"},{"title":"THUDM/GLM","url":"https://github.com/THUDM/GLM"},{"title":"jackaduma/ChatGLM-LoRA-RLHF-PyTorch","url":"https://github.com/jackaduma/ChatGLM-LoRA-RLHF-PyTorch"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm"},{"title":"2023-MindSpore-4/Code12","url":"https://github.com/2023-MindSpore-4/Code12/tree/main/MindFormers/glm3"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/glm-130b-an-open-bilingual-pre-trained-model","title":"GLM-130B: An Open Bilingual Pre-trained Model","date":"2022-10-05","rows_on_this_dataset":10,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":5,"samples_unverified":16,"pointer_only_for_licence":0,"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":1,"samples_harvested":21,"samples_ran":5,"samples_unverified":16,"pointer_only_for_licence":0,"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."}