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"language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": 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"https://huggingface.co/datasets/fancyzhx/dbpedia_14/blob/9abd46cf7fc8b4c64290f26993c540b92aa145ac/README.md", "name": "fancyzhx/dbpedia_14", "revision": "9abd46cf7fc8b4c64290f26993c540b92aa145ac", "url": "https://huggingface.co/datasets/fancyzhx/dbpedia_14" } ], "renewal_disposition": "include_with_caveat", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "choice": 100 } }, "source_reference": "SOURCES.md#dbpedia" }, "defeasible_nli": { "description": "追加情報によって推論の支持が強まるか弱まるかを扱う、撤回可能な推論のデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": 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{ "_type": "List", "feature": { "annotation_kind": { "_type": "Value", "dtype": "string" }, "decision_id": { "_type": "Value", "dtype": "string" }, "ids": { "_type": "List", "feature": { "_type": "Value", "dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "defeasible_nli", "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 100 }, "counts": { "cases": 100, "decisions": 100, "labeled_decisions": 100 }, "decision_types": { "choice": 100 } } }, "task_types": { "choice": 100 }, "upstream_datasets": [ { "license": [ "apache-2.0", "cc-by-sa-4.0" ], "license_source": "https://huggingface.co/datasets/tasksource/defeasible-nli/blob/7c4a57df9d8de5c36d4e9caa977907b5e8469c4f/README.md", "name": "tasksource/defeasible-nli", "revision": 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"Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": "large_string" } }, "input_hash": { "_type": "Value", "dtype": "string" }, "language": { "_type": "Value", "dtype": "string" }, "legacy_aux_json": { "_type": "Value", "dtype": "large_string" }, "provenance_json": { "_type": "Value", "dtype": "large_string" }, "schema_version": { "_type": "Value", "dtype": "string" }, "split": { "_type": "Value", "dtype": "string" }, "targets": { "_type": "List", "feature": { "annotation_kind": { "_type": "Value", "dtype": "string" }, "decision_id": { "_type": "Value", "dtype": "string" }, "ids": { "_type": "List", "feature": { "_type": "Value", "dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "esci", "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 100 }, "counts": { "cases": 100, "decisions": 100, "labeled_decisions": 100 }, "decision_types": { "choice": 100 } } }, "task_types": { "choice": 100 }, "upstream_datasets": [ { "license": [ "apache-2.0" ], "license_source": "https://huggingface.co/datasets/tasksource/esci/blob/8113b17a5d4099e20243282c926f1bc1a08a4d13/README.md", "name": "tasksource/esci", "revision": "8113b17a5d4099e20243282c926f1bc1a08a4d13", "url": "https://huggingface.co/datasets/tasksource/esci" } ], "renewal_disposition": "include", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "choice": 100 } }, "source_reference": "SOURCES.md#esci" }, "ethics": { "description": "日常的な行為や判断を倫理の観点から評価する、複数の課題を含むデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": 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100, "labeled_decisions": 100 }, "decision_types": { "choice": 18, "noul": 82 } } }, "task_types": { "choice": 18, "noul": 82 }, "upstream_datasets": [ { "license": [ "mit" ], "license_source": "https://huggingface.co/datasets/hendrycks/ethics/blob/b8b47c589f8bee77175b8648e5497278b68da48a/README.md", "name": "hendrycks/ethics", "revision": "b8b47c589f8bee77175b8648e5497278b68da48a", "url": "https://huggingface.co/datasets/hendrycks/ethics" } ], "renewal_disposition": "include", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "noul": 82, "choice": 18 } }, "source_reference": "SOURCES.md#ethics" }, "ethos": { "description": "オンラインの発言に含まれるヘイト表現やその属性を判定するデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": "large_string" } }, "input_hash": { "_type": "Value", "dtype": "string" }, "language": { "_type": "Value", "dtype": "string" }, "legacy_aux_json": { "_type": "Value", "dtype": "large_string" }, "provenance_json": { "_type": "Value", "dtype": "large_string" }, "schema_version": { "_type": "Value", "dtype": "string" }, "split": { "_type": "Value", "dtype": "string" }, "targets": { "_type": "List", "feature": { "annotation_kind": { "_type": "Value", "dtype": "string" }, "decision_id": { "_type": "Value", "dtype": "string" }, "ids": { "_type": "List", "feature": { "_type": "Value", "dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "ethos", "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 100 }, "counts": { "cases": 100, "decisions": 100, "labeled_decisions": 100 }, "decision_types": { "noul": 100 } } }, "task_types": { "noul": 100 }, "upstream_datasets": [ { "license": [ "mit" ], "license_source": "https://huggingface.co/datasets/SetFit/ethos_binary/blob/3e20849175072aa95845981f27938ebeb4eb93e0/README.md", "name": "SetFit/ethos_binary", "revision": "3e20849175072aa95845981f27938ebeb4eb93e0", "url": "https://huggingface.co/datasets/SetFit/ethos_binary" } ], "renewal_disposition": "include_with_caveat", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "noul": 100 } }, "source_reference": "SOURCES.md#ethos" }, "few_nerd": { "description": "文中のエンティティを細かな意味カテゴリで識別する、固有表現認識由来のデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 358 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": "large_string" } }, "input_hash": { "_type": "Value", "dtype": "string" }, "language": { "_type": "Value", "dtype": "string" }, "legacy_aux_json": { "_type": "Value", "dtype": "large_string" }, "provenance_json": { "_type": "Value", "dtype": "large_string" }, "schema_version": { "_type": "Value", "dtype": "string" }, "split": { "_type": "Value", "dtype": "string" }, "targets": { "_type": "List", "feature": { "annotation_kind": { "_type": "Value", "dtype": "string" }, "decision_id": { "_type": "Value", "dtype": "string" }, "ids": { "_type": "List", "feature": { "_type": "Value", "dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "few_nerd", "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 358 }, "counts": { "cases": 100, "decisions": 358, "labeled_decisions": 358 }, "decision_types": { "choice": 358 } } }, "task_types": { "choice": 358 }, "upstream_datasets": [ { "license": [ "cc-by-sa-4.0" ], "license_source": "https://huggingface.co/datasets/DFKI-SLT/few-nerd/blob/205f3e9c9f3577ea2561d43f2f62dc249ab92d5b/README.md", "name": "DFKI-SLT/few-nerd", "revision": "205f3e9c9f3577ea2561d43f2f62dc249ab92d5b", "url": "https://huggingface.co/datasets/DFKI-SLT/few-nerd" } ], "renewal_disposition": "include_with_caveat", "renewed_statistics": { "cases": 100, "decisions": 358, "types": { "choice": 358 } }, "source_reference": "SOURCES.md#few_nerd" }, "fol_nli": { "description": "一階述語論理に関係する文の意味や帰結を扱う、論理的含意判断のデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": 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"dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "fol_nli", "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 100 }, "counts": { "cases": 100, "decisions": 100, "labeled_decisions": 100 }, "decision_types": { "choice": 100 } } }, "task_types": { "choice": 100 }, "upstream_datasets": [ { "license": [ "apache-2.0" ], "license_source": "https://huggingface.co/datasets/tasksource/FOL-nli/blob/6b8a2ec01b226ed871fbde4677a31146ac5f370c/README.md", "name": "tasksource/FOL-nli", "revision": "6b8a2ec01b226ed871fbde4677a31146ac5f370c", "url": "https://huggingface.co/datasets/tasksource/FOL-nli" } ], "renewal_disposition": "include", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "choice": 100 } }, "source_reference": 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This derived Noul evaluation is not official p-MRR.", "evaluation_suite": "robust04", "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 200 } }, "license_scope": "HF card licenses are recorded per source; original evaluation corpus redistribution terms are separate.", "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": "large_string" } }, "input_hash": { "_type": "Value", "dtype": "string" }, "language": { "_type": "Value", "dtype": "string" }, "legacy_aux_json": { "_type": "Value", "dtype": "large_string" }, "provenance_json": { "_type": "Value", "dtype": "large_string" }, "schema_version": { "_type": "Value", "dtype": "string" }, "split": { "_type": "Value", "dtype": "string" }, "targets": { "_type": "List", "feature": { "annotation_kind": { "_type": "Value", "dtype": "string" }, "decision_id": { "_type": "Value", "dtype": "string" }, "ids": { "_type": "List", "feature": { "_type": "Value", "dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "followir", "structured_statistics": { "test": { "annotation_kinds": { "derived_relevance_label": 200 }, "counts": { "cases": 100, "decisions": 200, "labeled_decisions": 200 }, "decision_types": { "noul": 200 } } }, "task_types": { "noul": 200 }, "teacher_provenance": "Train: GPT-3.5 synthetic documents/labels filtered by Mistral; preserve source label, retain Mistral score only as metadata. 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"_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": "large_string" } }, "input_hash": { "_type": "Value", "dtype": "string" }, "language": { "_type": "Value", "dtype": "string" }, "legacy_aux_json": { "_type": "Value", "dtype": "large_string" }, "provenance_json": { "_type": "Value", "dtype": "large_string" }, "schema_version": { "_type": "Value", "dtype": "string" }, 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"revision": "09be8c5bbc57cb3887f3a9732ad6aa7ec602a1fa", "url": "https://huggingface.co/datasets/Anthropic/hh-rlhf" } ], "renewal_disposition": "include_with_caveat", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "choice": 100 } }, "source_reference": "SOURCES.md#hh_rlhf" }, "hwu64": { "description": "複数の領域にまたがるアシスタント向け発話から、その意図を分類するデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": "large_string" } }, "input_hash": { "_type": "Value", "dtype": "string" }, "language": { "_type": "Value", "dtype": "string" }, "legacy_aux_json": { "_type": "Value", "dtype": "large_string" }, "provenance_json": { "_type": "Value", "dtype": "large_string" }, "schema_version": { "_type": "Value", "dtype": "string" }, "split": { "_type": "Value", "dtype": "string" }, "targets": { "_type": "List", "feature": { "annotation_kind": { "_type": "Value", "dtype": "string" }, "decision_id": { "_type": "Value", "dtype": "string" }, "ids": { "_type": "List", "feature": { "_type": "Value", "dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "hwu64", "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 100 }, "counts": { "cases": 100, "decisions": 100, "labeled_decisions": 100 }, "decision_types": { "choice": 100 } } }, "task_types": { "choice": 100 }, "upstream_datasets": [ { "license": [ "cc-by-sa-3.0" ], "license_source": "https://huggingface.co/datasets/FastFit/hwu_64/blob/9fb5bff1e37c4c5a6d46c1ff6f286b6d3f362222/README.md", "name": "FastFit/hwu_64", "revision": "9fb5bff1e37c4c5a6d46c1ff6f286b6d3f362222", "url": "https://huggingface.co/datasets/FastFit/hwu_64" } ], "renewal_disposition": "include_with_caveat", "renewed_statistics": { "cases": 100, "decisions": 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"_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": "large_string" } }, "input_hash": { "_type": "Value", "dtype": "string" }, "language": { "_type": "Value", "dtype": "string" }, "legacy_aux_json": { "_type": "Value", "dtype": "large_string" }, "provenance_json": { "_type": "Value", "dtype": "large_string" }, "schema_version": { "_type": "Value", "dtype": "string" }, "split": { "_type": "Value", "dtype": "string" }, "targets": { "_type": "List", "feature": { "annotation_kind": { "_type": "Value", "dtype": "string" }, "decision_id": { "_type": "Value", "dtype": "string" }, "ids": { "_type": "List", "feature": { "_type": "Value", "dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "impli", "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 100 }, "counts": { "cases": 100, "decisions": 100, "labeled_decisions": 100 }, "decision_types": { "noul": 100 } } }, "task_types": { "noul": 100 }, "upstream_datasets": [ { "license": [ "apache-2.0" ], "license_source": "https://github.com/UKPLab/acl2022-impli/blob/d43c272c895c773b311018d51ab65f592c6699bb/README.md", "name": "https://github.com/UKPLab/acl2022-impli", "revision": "d43c272c895c773b311018d51ab65f592c6699bb", "url": "https://github.com/UKPLab/acl2022-impli" } ], "renewal_disposition": "include", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "noul": 100 } }, "source_reference": "SOURCES.md#impli" }, "lexcomp": { "description": "複合語の意味を候補の言い換えが正しく説明するか、文脈を踏まえて判断するデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": 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100, "decisions": 100, "labeled_decisions": 100 }, "decision_types": { "noul": 100 } } }, "task_types": { "noul": 100 }, "upstream_datasets": [ { "license": [ "apache-2.0" ], "license_source": "https://huggingface.co/datasets/tasksource/lexcomp-nc-relation/blob/4f7fa5d487d71ac1543bc7849993b5dfc2e361ac/README.md", "name": "tasksource/lexcomp-nc-relation", "revision": "4f7fa5d487d71ac1543bc7849993b5dfc2e361ac", "url": "https://huggingface.co/datasets/tasksource/lexcomp-nc-relation" } ], "renewal_disposition": "include", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "noul": 100 } }, "source_reference": "SOURCES.md#lexcomp" }, "logical_entailment": { "description": "提示された論理的な前提から結論が導けるかを判断する、含意推論のデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" }, "value": { "_type": "Value", "dtype": "float64" } } }, "documents": { "_type": "List", "feature": { "content_json": { "_type": "Value", "dtype": "large_string" }, "id": { "_type": "Value", "dtype": "string" } } }, "id": { "_type": "Value", "dtype": "string" }, "instructions_json": { "_type": "Value", "dtype": "large_string" }, "kind": { "_type": "Value", "dtype": "string" }, "scoring": { "_type": "Value", "dtype": "string" }, "system_prompt": { "_type": "Value", "dtype": "large_string" }, "type": { "_type": "Value", "dtype": "string" } } }, "state_json": { "_type": "Value", "dtype": "large_string" } }, "input_hash": { "_type": "Value", "dtype": "string" }, "language": { "_type": "Value", "dtype": "string" }, "legacy_aux_json": { "_type": "Value", "dtype": "large_string" }, "provenance_json": { "_type": "Value", "dtype": "large_string" }, "schema_version": { "_type": "Value", "dtype": "string" }, "split": { "_type": "Value", "dtype": "string" }, "targets": { "_type": "List", "feature": { "annotation_kind": { "_type": "Value", "dtype": "string" }, "decision_id": { "_type": "Value", "dtype": "string" }, "ids": { "_type": "List", "feature": { "_type": "Value", "dtype": "string" } }, "kind": { "_type": "Value", "dtype": "string" }, "metadata_json": { "_type": "Value", "dtype": "large_string" }, "probabilities": { "_type": "List", "feature": { "_type": "Value", "dtype": "float64" } } } } }, "schema_version": "system_one.v1", "source_subset": "logical_entailment", "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 100 }, "counts": { "cases": 100, "decisions": 100, "labeled_decisions": 100 }, "decision_types": { "noul": 100 } } }, "task_types": { "noul": 100 }, "upstream_datasets": [ { "license": [ "apache-2.0" ], "license_source": "https://huggingface.co/datasets/tasksource/logical-entailment/blob/5275a96cea765dc3bbfd41e6034485791704ed69/README.md", "name": "tasksource/logical-entailment", "revision": "5275a96cea765dc3bbfd41e6034485791704ed69", "url": "https://huggingface.co/datasets/tasksource/logical-entailment" } ], "renewal_disposition": "include", "renewed_statistics": { "cases": 100, "decisions": 100, "types": { "noul": 100 } }, "source_reference": "SOURCES.md#logical_entailment" }, "lonli": { "description": "さまざまな論理現象を制御した文対で、含意・矛盾などの判断を評価するデータセット。元の注釈をChoice・Noul・Scoreの該当形式で保持し、instructionと学習・評価用の分割を収録しています。", "evaluation_suite": null, "language": [ "en" ], "legacy_statistics": { "test": { "cases": 100, "decisions": 100 } }, "schema": { "case_id": { "_type": "Value", "dtype": "string" }, "group_id": { "_type": "Value", "dtype": "string" }, "input": { "decisions": { "_type": "List", "feature": { "criteria": { "_type": "List", "feature": { "description_json": { "_type": "Value", "dtype": 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author = {Zheng, Tianyu and Zhang, Ge and Shen, Tianhao and Liu, Xueling and Lin, Bill Yuchen and Fu, Jie and Chen, Wenhu and Yue, Xiang},\n year = {2024},\n eprint = {2402.14658},\n archivePrefix = {arXiv},\n url = {https://arxiv.org/abs/2402.14658},\n doi = {10.48550/arXiv.2402.14658}\n}\n", "category": "code", "citation_keys": [ "li2025coir", "zheng2024opencodeinterpreter" ], "description": "Retrieves the next relevant code-assistant response from long multi-turn programming conversations adapted from OpenCodeInterpreter-style feedback data.", "document_text_stats": { "count": 10000, "max_chars": 9402, "mean_chars": 1468.1565, "median_chars": 1373.0, "min_chars": 5 }, "language": "en", "language_detection": { "detector": "fast-langdetect", "document": { "languages": { "en": 99.34 }, "sample_count": 10000 }, "main_language_percent": 10.0, "min_language_percent": 0.5, "query": { "languages": { "en": 100.0 }, "sample_count": 200 } }, "languages": [ "en" ], "query_text_stats": { "count": 200, "max_chars": 21256, "mean_chars": 4468.625, "median_chars": 3662.5, "min_chars": 285 }, "references": [ { "authors": [ "Xiangyang Li", "Kuicai Dong", "Yi Quan Lee", "Wei Xia", "Hao Zhang", "Xinyi Dai", "Yasheng Wang", "Ruiming Tang" ], "doi": "10.18653/v1/2025.acl-long.1072", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CoIR: A Comprehensive Benchmark for Code Information Retrieval Models", "url": "https://aclanthology.org/2025.acl-long.1072/", "year": 2025 }, { "authors": [ "Tianyu Zheng", "Ge Zhang", "Tianhao Shen", "Xueling Liu", "Bill Yuchen Lin", "Jie Fu", "Wenhu Chen", "Xiang Yue" ], "doi": "10.48550/arXiv.2402.14658", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement", "url": "https://arxiv.org/abs/2402.14658", "year": 2024 } ], "short_description": "Multi-turn code feedback retrieval." }, "NanoCodeFeedbackST": { "bibtex": "@inproceedings{li2025coir,\n title = {{CoIR}: A Comprehensive Benchmark for Code Information Retrieval Models},\n author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},\n booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages = {22074--22091},\n year = {2025},\n address = {Vienna, Austria},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2025.acl-long.1072/},\n doi = {10.18653/v1/2025.acl-long.1072}\n}\n", "category": "code", "citation_keys": [ "li2025coir" ], "description": "Retrieves code-assistant responses for single-turn programming instructions from the CoIR CodeFeedback-ST task.", "document_text_stats": { "count": 10000, "max_chars": 11353, "mean_chars": 1538.7276, "median_chars": 1407.0, "min_chars": 1 }, "language": "en", "language_detection": { "detector": "fast-langdetect", "document": { "languages": { "en": 98.08 }, "sample_count": 10000 }, "main_language_percent": 10.0, "min_language_percent": 0.5, "query": { "languages": { "en": 100.0 }, "sample_count": 200 } }, "languages": [ "en" ], "query_text_stats": { "count": 200, "max_chars": 3406, "mean_chars": 730.515, "median_chars": 598.5, "min_chars": 57 }, "references": [ { "authors": [ "Xiangyang Li", "Kuicai Dong", "Yi Quan Lee", "Wei Xia", "Hao Zhang", "Xinyi Dai", "Yasheng Wang", "Ruiming Tang" ], "doi": "10.18653/v1/2025.acl-long.1072", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CoIR: A Comprehensive Benchmark for Code Information Retrieval Models", "url": "https://aclanthology.org/2025.acl-long.1072/", "year": 2025 } ], "short_description": "Single-turn code feedback retrieval." }, "NanoCodeSearchNet": { "bibtex": "@inproceedings{li2025coir,\n title = {{CoIR}: A Comprehensive Benchmark for Code Information Retrieval Models},\n author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},\n booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages = {22074--22091},\n year = {2025},\n address = {Vienna, Austria},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2025.acl-long.1072/},\n doi = {10.18653/v1/2025.acl-long.1072}\n}\n\n@article{husain2019codesearchnet,\n title = {{CodeSearchNet} Challenge: Evaluating the State of Semantic Code Search},\n author = {Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal = {arXiv preprint arXiv:1909.09436},\n year = {2019},\n url = {https://arxiv.org/abs/1909.09436},\n doi = {10.48550/arXiv.1909.09436}\n}\n", "category": "code", "citation_keys": [ "li2025coir", "husain2019codesearchnet" ], "description": "Uses CodeSearchNet pairs to retrieve the natural-language summary or documentation associated with a code snippet.", "document_text_stats": { "count": 10000, "max_chars": 2610, "mean_chars": 86.072, "median_chars": 65.0, "min_chars": 8 }, "language": "en", "language_detection": { "detector": "fast-langdetect", "document": { "languages": { "en": 94.34, "es": 1.5, "ru": 1.0, "uk": 0.58, "zh": 0.51 }, "sample_count": 10000 }, "main_language_percent": 10.0, "min_language_percent": 0.5, "query": { "languages": { "ceb": 0.5, "en": 85.5, "es": 1.0, "hu": 0.5, "nl": 0.5, "pt": 5.5, "ru": 2.5, "tr": 1.5, "uk": 0.5, "zh": 2.0 }, "sample_count": 200 } }, "languages": [ "en" ], "query_text_stats": { "count": 200, "max_chars": 3345, "mean_chars": 636.265, "median_chars": 434.0, "min_chars": 83 }, "references": [ { "authors": [ "Xiangyang Li", "Kuicai Dong", "Yi Quan Lee", "Wei Xia", "Hao Zhang", "Xinyi Dai", "Yasheng Wang", "Ruiming Tang" ], "doi": "10.18653/v1/2025.acl-long.1072", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CoIR: A Comprehensive Benchmark for Code Information Retrieval Models", "url": "https://aclanthology.org/2025.acl-long.1072/", "year": 2025 }, { "authors": [ "Hamel Husain", "Ho-Hsiang Wu", "Tiferet Gazit", "Miltiadis Allamanis", "Marc Brockschmidt" ], "doi": "10.48550/arXiv.1909.09436", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search", "url": "https://arxiv.org/abs/1909.09436", "year": 2019 } ], "short_description": "CodeSearchNet code-summary retrieval." }, "NanoCodeSearchNetCCR": { "bibtex": "@inproceedings{li2025coir,\n title = {{CoIR}: A Comprehensive Benchmark for Code Information Retrieval Models},\n author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},\n booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages = {22074--22091},\n year = {2025},\n address = {Vienna, Austria},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2025.acl-long.1072/},\n doi = {10.18653/v1/2025.acl-long.1072}\n}\n", "category": "code", "citation_keys": [ "li2025coir" ], "description": "Retrieves continuation code for partial function contexts in CoIR's CodeSearchNet-CCR code-to-code setting.", "document_text_stats": { "count": 10000, "max_chars": 1849, "mean_chars": 158.4226, "median_chars": 99.0, "min_chars": 23 }, "language": "en", "language_detection": { "detector": "fast-langdetect", "document": { "languages": { "ar": 0.74, "de": 1.74, "en": 78.37, "es": 3.42, "fa": 1.09, "fr": 0.57, "it": 1.26, "ja": 2.0, "pt": 0.68, "ru": 2.48, "uk": 1.35, "zh": 1.71 }, "sample_count": 10000 }, "main_language_percent": 10.0, "min_language_percent": 0.5, "query": { "languages": { "ceb": 0.5, "en": 86.0, "es": 1.0, "hu": 0.5, "pt": 5.5, "ru": 2.5, "tr": 1.5, "uk": 0.5, "zh": 2.0 }, "sample_count": 200 } }, "languages": [ "en" ], "query_text_stats": { "count": 200, "max_chars": 2240, "mean_chars": 372.82, "median_chars": 237.0, "min_chars": 47 }, "references": [ { "authors": [ "Xiangyang Li", "Kuicai Dong", "Yi Quan Lee", "Wei Xia", "Hao Zhang", "Xinyi Dai", "Yasheng Wang", "Ruiming Tang" ], "doi": "10.18653/v1/2025.acl-long.1072", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CoIR: A Comprehensive Benchmark for Code Information Retrieval Models", "url": "https://aclanthology.org/2025.acl-long.1072/", "year": 2025 } ], "short_description": "CodeSearchNet code-context retrieval." }, "NanoCodeTransOceanContest": { "bibtex": "@inproceedings{li2025coir,\n title = {{CoIR}: A Comprehensive Benchmark for Code Information Retrieval Models},\n author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},\n booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages = {22074--22091},\n year = {2025},\n address = {Vienna, Austria},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2025.acl-long.1072/},\n doi = {10.18653/v1/2025.acl-long.1072}\n}\n\n@inproceedings{yan2023codetransocean,\n title = {{CodeTransOcean}: A Comprehensive Multilingual Benchmark for Code Translation},\n author = {Yan, Weixiang and Tian, Yuchen and Li, Yunzhe and Chen, Qian and Wang, Wen},\n booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},\n pages = {5067--5089},\n year = {2023},\n address = {Singapore},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2023.findings-emnlp.337/},\n doi = {10.18653/v1/2023.findings-emnlp.337}\n}\n", "category": "code", "citation_keys": [ "li2025coir", "yan2023codetransocean" ], "description": "Uses CodeTransOcean contest data 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Comprehensive Benchmark for Code Information Retrieval Models", "url": "https://aclanthology.org/2025.acl-long.1072/", "year": 2025 }, { "authors": [ "Weixiang Yan", "Yuchen Tian", "Yunzhe Li", "Qian Chen", "Wen Wang" ], "doi": "10.18653/v1/2023.findings-emnlp.337", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation", "url": "https://aclanthology.org/2023.findings-emnlp.337/", "year": 2023 } ], "short_description": "Cross-language contest code retrieval." }, "NanoCodeTransOceanDL": { "bibtex": "@inproceedings{li2025coir,\n title = {{CoIR}: A Comprehensive Benchmark for Code Information Retrieval Models},\n author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},\n booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages = {22074--22091},\n year = {2025},\n address = {Vienna, Austria},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2025.acl-long.1072/},\n doi = {10.18653/v1/2025.acl-long.1072}\n}\n\n@inproceedings{yan2023codetransocean,\n title = {{CodeTransOcean}: A Comprehensive Multilingual Benchmark for Code Translation},\n author = {Yan, Weixiang and Tian, Yuchen and Li, Yunzhe and Chen, Qian and Wang, Wen},\n booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},\n pages = {5067--5089},\n year = {2023},\n address = {Singapore},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2023.findings-emnlp.337/},\n doi = {10.18653/v1/2023.findings-emnlp.337}\n}\n", "category": "code", "citation_keys": [ "li2025coir", "yan2023codetransocean" ], "description": "Uses CodeTransOcean-DL examples to retrieve equivalent Python deep-learning code across machine-learning frameworks.", 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"authors": [ "Weixiang Yan", "Yuchen Tian", "Yunzhe Li", "Qian Chen", "Wen Wang" ], "doi": "10.18653/v1/2023.findings-emnlp.337", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation", "url": "https://aclanthology.org/2023.findings-emnlp.337/", "year": 2023 } ], "short_description": "Deep-learning framework code retrieval." }, "NanoCosQA": { "bibtex": "@inproceedings{li2025coir,\n title = {{CoIR}: A Comprehensive Benchmark for Code Information Retrieval Models},\n author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},\n booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages = {22074--22091},\n year = {2025},\n address = {Vienna, Austria},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2025.acl-long.1072/},\n doi = {10.18653/v1/2025.acl-long.1072}\n}\n\n@inproceedings{huang2021cosqa,\n title = {{CoSQA}: 20,000+ Web Queries for Code Search and Question Answering},\n author = {Huang, Junjie and Tang, Duyu and Shou, Linjun and Gong, Ming and Xu, Ke and Jiang, Daxin and Zhou, Ming and Duan, Nan},\n booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},\n pages = {5690--5700},\n year = {2021},\n address = {Online},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2021.acl-long.442/},\n doi = {10.18653/v1/2021.acl-long.442}\n}\n", "category": "code", "citation_keys": [ "li2025coir", "huang2021cosqa" ], "description": "Uses CoSQA web queries to retrieve relevant Python code, testing short natural-language code search with lexical and semantic gaps.", 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"https://aclanthology.org/2025.acl-long.1072/", "year": 2025 }, { "authors": [ "Junjie Huang", "Duyu Tang", "Linjun Shou", "Ming Gong", "Ke Xu", "Daxin Jiang", "Ming Zhou", "Nan Duan" ], "doi": "10.18653/v1/2021.acl-long.442", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CoSQA: 20,000+ Web Queries for Code Search and Question Answering", "url": "https://aclanthology.org/2021.acl-long.442/", "year": 2021 } ], "short_description": "Web-query-to-code retrieval." }, "NanoStackOverflowQA": { "bibtex": "@inproceedings{li2025coir,\n title = {{CoIR}: A Comprehensive Benchmark for Code Information Retrieval Models},\n author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},\n booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages = {22074--22091},\n year = {2025},\n address = {Vienna, Austria},\n publisher 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"Ruiming Tang" ], "doi": "10.18653/v1/2025.acl-long.1072", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CoIR: A Comprehensive Benchmark for Code Information Retrieval Models", "url": "https://aclanthology.org/2025.acl-long.1072/", "year": 2025 } ], "short_description": "StackOverflow code QA retrieval." }, "NanoSyntheticText2SQL": { "bibtex": "@inproceedings{li2025coir,\n title = {{CoIR}: A Comprehensive Benchmark for Code Information Retrieval Models},\n author = {Li, Xiangyang and Dong, Kuicai and Lee, Yi Quan and Xia, Wei and Zhang, Hao and Dai, Xinyi and Wang, Yasheng and Tang, Ruiming},\n booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n pages = {22074--22091},\n year = {2025},\n address = {Vienna, Austria},\n publisher = {Association for Computational Linguistics},\n url = {https://aclanthology.org/2025.acl-long.1072/},\n doi = 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"sample_count": 10000 }, "main_language_percent": 10.0, "min_language_percent": 0.5, "query": { "languages": { "en": 100.0 }, "sample_count": 200 } }, "languages": [ "en" ], "query_text_stats": { "count": 200, "max_chars": 188, "mean_chars": 102.935, "median_chars": 99.0, "min_chars": 49 }, "references": [ { "authors": [ "Xiangyang Li", "Kuicai Dong", "Yi Quan Lee", "Wei Xia", "Hao Zhang", "Xinyi Dai", "Yasheng Wang", "Ruiming Tang" ], "doi": "10.18653/v1/2025.acl-long.1072", "is_paper": true, "source_confidence": "definitive_paper_link", "title": "CoIR: A Comprehensive Benchmark for Code Information Retrieval Models", "url": "https://aclanthology.org/2025.acl-long.1072/", "year": 2025 }, { "authors": [ "Yev Meyer", "Marjan Emadi", "Dhruv Nathawani", "Lipika Ramaswamy", "Kendrick Boyd", "Maarten Van Segbroeck", "Matthew Grossman", "Piotr Mlocek", "Drew Newberry" ], "is_paper": false, "source_confidence": "probably_correct", "title": "Synthetic-Text-To-SQL: A synthetic dataset for training language models to generate SQL queries from natural language prompts", "url": "https://huggingface.co/datasets/gretelai/synthetic_text_to_sql", "year": 2024 } ], "short_description": "Text-to-SQL retrieval." } }, "structured_statistics": { "test": { "annotation_kinds": { "hard_label": 800 }, "counts": { "cases": 800, "decisions": 800, "labeled_decisions": 800 }, "decision_types": { "score": 800 } } }, "task_types": { "score": 800, "noul": 800 }, "upstream_datasets": [ { "license": "unknown", "name": "NanoApps", "revision": "f6fa3c5c630d51870e260d00ebccdb32abb0afb6", "url": "https://huggingface.co/datasets/hakari-bench/NanoCoIR" }, { "license": "unknown", "name": "NanoCodeFeedbackMT", "revision": "f6fa3c5c630d51870e260d00ebccdb32abb0afb6", "url": "https://huggingface.co/datasets/hakari-bench/NanoCoIR" }, { "license": "unknown", "name": "NanoCodeFeedbackST", "revision": "f6fa3c5c630d51870e260d00ebccdb32abb0afb6", "url": "https://huggingface.co/datasets/hakari-bench/NanoCoIR" }, 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