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Dataset Card for Signed Leases with Clause Marks

Signed Leases with Clause Marks

Every page of signed rental and lease agreements from 36 Western countries, main clauses marked, tenant summary added.

Collected through the Kled app by consenting contributors and licensed for commercial training. The files are delivered by Kled under license, not downloaded from this repository. Use Request a sample set at the top of this page, or write to team@kled.ai. Catalog entry: kled.ai/datasets/rental-agreements.

Dataset Details

Dataset Description

Upload the agreement you signed, every page in order, mark where the main clauses begin, and write a paragraph in your own words on what it does; templates, partial documents, other contract types and copied summaries are refused. Collection is open only in the US, Canada, the UK, Europe, Australia and New Zealand and capped at 200 contributors, so the set is deliberately small and deeply covered: one document class, clause-level comparability across jurisdictions, and a human summary on every document, a supervised summarization corpus by construction. Public contract corpora are American, corporate and heterogeneous (CUAD is 510 commercial contracts); a rental agreement is long, highly structured and rarely available signed.

  • Curated by: Kled (useKled)
  • Collected through: File upload from phone
  • Language(s): English, French, German, Spanish, Italian, Dutch
  • Regions: US, Canada, UK, Europe, Australia and New Zealand (36 countries)
  • Volume: Available on request
  • Contributors: Available on request
  • Modality: Text
  • Format: PDF
  • Resolution: Native PDF, text layer where the source system produced one
  • Annotations: Captioned (clause markers, tenant summary)
  • Quality control: QA review on every submission
  • License: Non-exclusive commercial training license (terms); exclusivity available on request
  • Consent: Each contributor opted in to this use when they uploaded.

Dataset Sources

Uses

Direct Use

  • Long-context legal reasoning
  • Clause extraction across jurisdictions
  • Supervised contract summarization

Out-of-Scope Use

Redistribution or resale of the files, use outside the terms of the commercial training license, and any attempt to identify or contact contributors or people who appear in the data. Uses that require labels this set does not carry should be scoped with the data team first.

Dataset Structure

Data Instances

One document per record, delivered as PDF. Volume: Available on request.

Data Fields

  • File: the PDF file as captured or uploaded by the contributor
  • Labels: Captioned (clause markers, tenant summary), entered by the contributor at capture and checked at review.
  • Capture metadata: capture tool, country of capture, submission time
  • Review status: QA outcome (QA review on every submission)

Field names and the manifest format are confirmed with the sample set.

Data Splits

Delivered as one set. Subsets by country, language or label, and custom splits, are available on request.

Dataset Creation

Curation Rationale

The dataset exists because the public alternatives named in the description are non-commercial, synthetic, small or collected elsewhere. Kled runs the task as a paid brief in its app so that the data is captured on purpose, under the capture rules above, by people who have opted in to commercial use.

Source Data

Data Collection and Processing

Contributors complete the task in the Kled app. Capture tool: File upload from phone. Each submission is reviewed against the task brief; submissions that break the capture rules are rejected. QA review on every submission. Files are delivered as PDF, in the resolution noted above.

Who are the source data producers?

Kled contributors, who complete paid data tasks on their own phones. This task was open to contributors in the US, Canada, the UK, Europe, Australia and New Zealand (36 countries). Kled has 625,000+ contributors in 100+ countries, strongest in Southeast Asia, the United States and Europe.

Annotations

Annotation process

Captioned (clause markers, tenant summary). The label is entered by the contributor as part of the task and checked at review against the task brief; it is a contributor label, not an expert annotation, unless the description above says otherwise.

Who are the annotators?

The contributors themselves, at the moment of capture. Kled reviewers check each submission against the task brief.

Personal and Sensitive Information

Every item was contributed by its owner, who opted in to this use at upload. The task brief states what may and may not appear in a submission, and reviewers reject submissions that break those rules. Kled's data handling practices are documented at trust.kled.ai; questions about handling requirements for your use go to team@kled.ai.

Bias, Risks, and Limitations

Coverage follows the contributor base: strongest in Southeast Asia, the United States and Europe, thinner elsewhere, and limited to the US, Canada, the UK, Europe, Australia and New Zealand (36 countries) for this task. Devices, settings and conditions vary by contributor, which is the point of the collection and also means no two items are captured under identical conditions. Where labels are contributor-entered, they are checked at review but are not expert annotations. Volumes are the current scope of the task and grow as collection continues.

Recommendations

Request a sample set and run it against your task before licensing. Ask for per-country or per-language counts where they matter to your use.

Licensing Information

Non-exclusive commercial training license; exclusivity on request. Each contributor opted in to this use when they uploaded. The license terms are on the catalog page.

Citation

@misc{kled_rental_agreements_2026,
  title        = {Signed Leases with Clause Marks},
  author       = {Kled},
  year         = {2026},
  url          = {https://www.kled.ai/datasets/rental-agreements},
  note         = {Commercially licensed dataset collected through the Kled app. Sample sets on request.}
}

Dataset Card Contact

Kled data team: team@kled.ai

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