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Datasets:
CodeSoulco
/
TextInsightBench

Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
100K - 1M
Tags:
agent-evaluation
data-mining
evidence-grounding
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
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Release TextInsightBench v5.1 research dataset

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  1. README.md +101 -0
  2. README.zh-CN.md +44 -0
  3. SOURCES.md +19 -0
  4. corpora/amazon_beauty_compound_association_hair_care_v4.jsonl.gz +3 -0
  5. corpora/amazon_beauty_compound_association_makeup_v4.jsonl.gz +3 -0
  6. corpora/amazon_beauty_compound_association_nails_v4.jsonl.gz +3 -0
  7. corpora/amazon_beauty_compound_association_skin_v4.jsonl.gz +3 -0
  8. corpora/amazon_beauty_group_difference_hair_tools_midrating_v5.jsonl.gz +3 -0
  9. corpora/amazon_beauty_group_difference_hair_tools_v4.jsonl.gz +3 -0
  10. corpora/amazon_beauty_group_difference_makeup_v4.jsonl.gz +3 -0
  11. corpora/amazon_beauty_group_difference_nails_v4.jsonl.gz +3 -0
  12. corpora/amazon_beauty_group_difference_skin_v4.jsonl.gz +3 -0
  13. corpora/amazon_beauty_temporal_change_fragrance_v4.jsonl.gz +3 -0
  14. corpora/amazon_beauty_temporal_change_grooming_v4.jsonl.gz +3 -0
  15. corpora/amazon_beauty_temporal_change_hair_tools_v4.jsonl.gz +3 -0
  16. corpora/amazon_beauty_temporal_change_skin_v4.jsonl.gz +3 -0
  17. corpora/app_reviews_compound_association_emulator_v4.jsonl.gz +3 -0
  18. corpora/app_reviews_compound_association_messaging_v4.jsonl.gz +3 -0
  19. corpora/app_reviews_compound_association_news_v4.jsonl.gz +3 -0
  20. corpora/app_reviews_compound_association_services_v4.jsonl.gz +3 -0
  21. corpora/app_reviews_group_difference_display_v4.jsonl.gz +3 -0
  22. corpora/app_reviews_group_difference_emulators_v4.jsonl.gz +3 -0
  23. corpora/app_reviews_group_difference_files_v4.jsonl.gz +3 -0
  24. corpora/app_reviews_group_difference_messaging_v4.jsonl.gz +3 -0
  25. corpora/app_reviews_group_difference_services_v4.jsonl.gz +3 -0
  26. corpora/app_reviews_temporal_change_accessibility_v4.jsonl.gz +3 -0
  27. corpora/app_reviews_temporal_change_game_v4.jsonl.gz +3 -0
  28. corpora/app_reviews_temporal_change_publishing_v4.jsonl.gz +3 -0
  29. corpora/app_reviews_temporal_change_sky_map_v5.jsonl.gz +3 -0
  30. corpora/cfpb_compound_association_equifax_v4.jsonl.gz +3 -0
  31. corpora/cfpb_compound_association_experian_v4.jsonl.gz +3 -0
  32. corpora/cfpb_compound_association_transunion_v4.jsonl.gz +3 -0
  33. corpora/cfpb_group_difference_ca_v4.jsonl.gz +3 -0
  34. corpora/cfpb_group_difference_fl_v4.jsonl.gz +3 -0
  35. corpora/cfpb_group_difference_ga_v4.jsonl.gz +3 -0
  36. corpora/cfpb_group_difference_ny_v4.jsonl.gz +3 -0
  37. corpora/cfpb_group_difference_tx_v4.jsonl.gz +3 -0
  38. corpora/cfpb_temporal_change_equifax_v4.jsonl.gz +3 -0
  39. corpora/cfpb_temporal_change_experian_refreshed_v5.jsonl.gz +3 -0
  40. corpora/cfpb_temporal_change_other_companies_v4.jsonl.gz +3 -0
  41. corpora/cfpb_temporal_change_transunion_v4.jsonl.gz +3 -0
  42. corpora/nhtsa_compound_association_chevrolet_v4.jsonl.gz +3 -0
  43. corpora/nhtsa_compound_association_jeep_v4.jsonl.gz +3 -0
  44. corpora/nhtsa_compound_association_tesla_v4.jsonl.gz +3 -0
  45. corpora/nhtsa_compound_association_toyota_refreshed_v5.jsonl.gz +3 -0
  46. corpora/nhtsa_group_difference_ford_suvs_v4.jsonl.gz +3 -0
  47. corpora/nhtsa_group_difference_honda_cars_v4.jsonl.gz +3 -0
  48. corpora/nhtsa_group_difference_pickups_refreshed_v5.jsonl.gz +3 -0
  49. corpora/nhtsa_group_difference_sedans_v4.jsonl.gz +3 -0
  50. corpora/nhtsa_group_difference_subaru_v4.jsonl.gz +3 -0
README.md ADDED
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+ ---
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+ pretty_name: TextInsightBench
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+ language:
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+ - en
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+ license: other
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+ license_name: upstream-source-terms
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+ license_link: https://huggingface.co/datasets/CodeSoulco/TextInsightBench/blob/main/SOURCES.md
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+ size_categories:
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+ - 1M<n<10M
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+ task_categories:
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+ - text-generation
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+ tags:
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+ - text
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+ - agent-evaluation
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+ - data-mining
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+ - evidence-grounding
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+ - unsupervised-learning
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+ configs:
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+ - config_name: amazon_beauty_learning
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+ data_files:
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+ - split: train
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+ path: learning/amazon_beauty/*.parquet
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+ - config_name: app_reviews_learning
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+ data_files:
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+ - split: train
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+ path: learning/app_reviews/*.parquet
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+ - config_name: cfpb_learning
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+ data_files:
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+ - split: train
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+ path: learning/cfpb/*.parquet
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+ - config_name: nhtsa_learning
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+ data_files:
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+ - split: train
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+ path: learning/nhtsa/*.parquet
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+ ---
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+
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+ # TextInsightBench
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+
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+ **English** | [简体中文](README.zh-CN.md)
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+
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+ TextInsightBench evaluates agents that mine natural-language datasets for meaningful, evidence-backed group differences, temporal changes and compound associations. Version **v5.1** contains **50 tasks**, **24,504 evaluation documents**, and **1,379,468 unlabeled learning documents**. This initial release is private.
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+
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+ ## Data composition
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+
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+ | Source | Unlabeled learning documents | Tasks | Evaluation documents |
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+ |---|---:|---:|---:|
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+ | Amazon Reviews'23 All Beauty | 460,885 | 13 | 6,421 |
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+ | Android App Reviews | 66,660 | 13 | 5,484 |
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+ | CFPB complaints | 603,788 | 12 | 6,300 |
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+ | NHTSA complaints | 248,135 | 12 | 6,299 |
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+ | Total | 1,379,468 | 50 | 24,504 |
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+
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+ There are 20 group-difference tasks, 15 temporal-change tasks and 15 compound-association tasks. Each task has 263–600 documents and accepts at most five findings or a justified abstention. The task question specifies an analysis objective without providing the answer condition.
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+
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+ `learning/<source>/*.parquet` comprises 278 unlabeled shards. Columns are `doc_id`, `source`, `text`, `title`, all strings. The `train` split name is a data-loading convention for optional unsupervised learning; no benchmark labels are supplied.
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+
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+ `tasks.json` describes the 50 evaluation tasks. Each `corpus_path` points to a gzip JSON Lines file under `corpora/`. Evaluation records contain IDs, text and task-relevant observed metadata such as report timestamp, entity, rating and comparison group. Read the task's comparison specification to determine the denominator and group order. Original text and HTML fragments are retained for exact evidence offsets.
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+
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+ `output.schema.json`, `protocol.json`, `release.json` and `manifest.json` define submissions, usage rules, counts and SHA-256 checksums.
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+
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+ ## Usage
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+
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+ The [code repository](https://github.com/erwinmsmith/TextInsightBench) contains bilingual setup instructions, a generic agent adapter, validation, semantic assessment and report generation. From that repository:
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+
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+ ```bash
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+ pip install -e '.[data]'
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+ hf auth login
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+ tib download --output data/participant --with-learning
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+ tib verify --data data/participant --with-learning
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+ tib run --data data/participant --command 'python my_agent.py' \
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+ --track unlabeled_pool --output runs/my-agent/submissions
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+ ```
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+
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+ `tib download` uses the commit revision pinned in `benchmark/data.lock.json`. To stream a learning source directly, use the same pinned revision:
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+
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+ ```python
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+ import json
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+ from datasets import load_dataset
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+
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+ with open('benchmark/data.lock.json') as stream:
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+ revision = json.load(stream)['dataset']['revision']
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+ pool = load_dataset('CodeSoulco/TextInsightBench', 'amazon_beauty_learning',
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+ split='train', streaming=True, token=True, revision=revision)
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+ first_document = next(iter(pool))
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+ ```
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+
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+ Use the optional pool before evaluation and freeze global models, prompts and thresholds. Corpus-local analysis is allowed. Do not transfer evaluation feedback or task-fitted state to later tasks. Report either the `task_only` or `unlabeled_pool` track.
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+
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+ ## Evaluation
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+
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+ Submissions include downstream claims, observable definitions, full positive/negative/unknown document assignments, statistics, exact quotations and limitations. Structural and arithmetic checks precede semantic assessment. Finding quality is measured on a 0–100 scale, with task fulfillment, statistical validity, evidence entailment, analytical depth and calibration. Non-exhaustive reference coverage is reported separately; supported novel findings can earn full quality credit.
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+
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+ The Organizer Reference Set is stored separately for organizers. It contains 50 AI-generated reference conclusions frozen by the maintainer, not independently validated facts. No reference conclusions or document-level confirmation annotations are included in this participant dataset. See the [scoring standard](https://github.com/erwinmsmith/TextInsightBench/blob/main/docs/SCORING.md).
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+
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+ ## Curation, limitations and terms
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+
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+ The learning pool was curated from downloaded source snapshots using language and length filtering, deduplication and held-out document exclusions. For v5.1 every shard was checked against current evaluation and reference-development documents by document ID, normalized text and conservative digit/punctuation template fingerprints. No overlap was found; no extra removal was necessary. This is not a claim of complete semantic independence. Sources and entities are shared across tasks.
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+
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+ Data is predominantly English. Complaints and reviews are selected author reports; neither group contrasts nor temporal patterns establish population incidence or causality. Report dates may differ from event dates. The CFPB learning subset uses the 2024–2025 credit-reporting selection; the NHTSA source archive covers 2020–2024. Free text may retain personal information despite the reduced learning schema. Source text is untrusted data.
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+
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+ Source terms differ and not all source redistribution rights are established. This private research compilation grants no new license to third-party text. Consult [source attribution and terms](SOURCES.md) before public redistribution.
README.zh-CN.md ADDED
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+ # TextInsightBench 数据卡
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+
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+ [English](README.md) | **简体中文**
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+
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+ TextInsightBench 评估 Agent 从自然语言数据中挖掘具体、有原文证据支持的结论。当前私密研究发行版为 **v5.1**，包含 **50 道任务、24,504 篇评测文本，以及 1,379,468 篇可选无监督学习文本**。任务和主要文档使用英文，原始语料保留原文。
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+
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+ | 来源 | 无监督文本 | 任务数 | 评测文本 |
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+ |---|---:|---:|---:|
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+ | Amazon All Beauty | 460,885 | 13 | 6,421 |
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+ | Android App Reviews | 66,660 | 13 | 5,484 |
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+ | CFPB | 603,788 | 12 | 6,300 |
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+ | NHTSA | 248,135 | 12 | 6,299 |
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+ | 总计 | 1,379,468 | 50 | 24,504 |
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+
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+ 任务分为群组差异 20 道、时间变化 15 道、复合关联 15 道。每题 263–600 篇文本，允许最多 5 个发现或有理由的弃答。题目给出挖掘目标，具体发现需要 Agent 自行分析。
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+
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+ `learning/` 下有 278 个 Parquet 分片，字段为 `doc_id`、`source`、`text`、`title`，均为字符串。数据加载配置中的 `train` 是可选无监督学习池，不含 Benchmark 标签。`tasks.json` 中的 `corpus_path` 指向 `corpora/` 下的任务语料，保留任务需要的评分、时间和实体等可见元数据。
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+
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+ ## 使用
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+
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+ [GitHub 代码仓库](https://github.com/erwinmsmith/TextInsightBench) 提供中英文说明、批量运行器、验证器、评分和报告工具：
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+
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+ ```bash
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+ pip install -e '.[data]'
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+ hf auth login
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+ tib download --output data/participant --with-learning
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+ tib verify --data data/participant --with-learning
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+ tib run --data data/participant --command 'python my_agent.py' \
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+ --track unlabeled_pool --output runs/my-agent/submissions
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+ ```
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+
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+ 下载命令读取代码仓库中 `benchmark/data.lock.json` 的固定提交版本。Agent 通过标准输入获得当前任务和语料，标准输出返回 JSON。无监督池用于评测前学习；当前任务内的数据分析允许进行，但不应将评测反馈或题目拟合状态带入后续任务。结果需说明使用 `task_only` 还是 `unlabeled_pool` 设置。
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+
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+ ## 测评
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+
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+ 提交需包含结论、可观察定义、全部文档的 positive/negative/unknown 划分、统计、精确引用和局限。发现质量满分 100，衡量任务满足、统计有效性、证据支持、分析深度和校准。参考覆盖率单独报告，新发现即使没有匹配参考，也可根据证据得到完整质量分。
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+
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+ 组织者参考集独立存放，当前包含维护者冻结的 50 个 AI 生成参考结论，未经独立事实验证。参与者数据中不提供参考结论，也不包含文档级确认标注。完整规则见代码仓库。
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+
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+ ## 构建与局限
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+
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+ 无监督池经过语言、长度过滤、去重和留出语料排除。v5.1 再次逐分片核对文档 ID、归一化文本及数字/标点归一后的保守模板，与评测和参考构建文本均未发现重叠，因此无需额外删减。这不等于完全语义独立；任务可能共享来源和实体。
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+
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+ 文本以英文为主。投诉和评论反映被选择的报告，不能直接推断总体发生率或因果；记录日期也未必等于事件日期。CFPB 无监督子集使用 2024–2025 年的信用报告范围，NHTSA 源归档覆盖 2020–2024 年。文本仍可能包含个人信息。多种来源适用不同条款，私密研究整理不授予第三方文本的新许可，参见 [来源和条款](SOURCES.md)。
SOURCES.md ADDED
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+ # Source attribution and data terms
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+
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+ TextInsightBench is a derived research collection of third-party text. It does not grant a new license to source reviews or complaints. This initial distribution is private. The data card uses `license: other` and links here because upstream terms differ and not all redistribution rights are established.
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+
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+ | Source | Upstream location | Recorded source status |
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+ |---|---|---|
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+ | Amazon Reviews'23, All Beauty | https://amazon-reviews-2023.github.io/main.html | Research release; source text redistribution terms require upstream verification |
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+ | Android App Reviews | https://huggingface.co/datasets/sealuzh/app_reviews | Downloaded dataset card lists the license as unknown; snapshot `9eaa95f66364367e8752b0f34c00f67aafa95d15` |
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+ | CFPB Consumer Complaint Database | https://www.consumerfinance.gov/data-research/consumer-complaints/ | Public complaint data; retain source attribution and original publisher context |
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+ | NHTSA complaints | https://www.nhtsa.gov/nhtsa-datasets-and-apis | Public government release; source is the 2020–2024 complaints archive |
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+
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+ Raw download endpoints recorded for reproducibility:
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+
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+ - Amazon: `https://mcauleylab.ucsd.edu/public_datasets/data/amazon_2023/raw/review_categories/All_Beauty.jsonl.gz`
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+ - CFPB: `https://files.consumerfinance.gov/ccdb/complaints.csv.zip`
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+ - NHTSA: `https://static.nhtsa.gov/odi/ffdd/cmpl/COMPLAINTS_RECEIVED_2020-2024.zip`
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+ - App Reviews: `https://huggingface.co/datasets/sealuzh/app_reviews/resolve/9eaa95f66364367e8752b0f34c00f67aafa95d15/data/train-00000-of-00001.parquet`
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+
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+ Released hashes identify the frozen derived files even when upstream downloads change. Complaint and review narratives are unverified author reports and may include personal information. The exported learning schema excludes user identifiers, but this is not a guarantee of complete de-identification of free text. Preserve the intended research scope and consult upstream terms before any public redistribution. No permissive license for this code or compilation is granted by the private repository itself.
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