datasheet.md

Datasheet: SFD — SEC Filings Dataset (v1)

Following the Datasheets for Datasets template (Gebru et al., 2021).

Motivation

For what purpose was the dataset created? SFD-v1 was created to provide a large, open, layout-faithful corpus of U.S. SEC EDGAR regulatory filings for long-context language modeling, financial reasoning research, document understanding, and contamination-resistant evaluation. Existing financial corpora (BloombergGPT/FinPile, BeanCounter, EDGAR-CORPUS) are either proprietary, narrowly scoped, or strip the visual table structure that financial reasoning depends on.

Who created the dataset? Anonymous Authors (Anonymous Organization).

Who funded the creation? Anonymous Organization.

Composition

What do the instances represent? Each instance is one parsed SEC EDGAR filing, represented as MultiMarkdown text plus metadata fields (accession number, year/month, source format, OCR flag, char count, MD5). Filings span 350+ form types including 10-K, 10-Q, 8-K, Form 4, N-PORT, 13F, 485BPOS, ABS-EE, and many others.

How many instances? Approximately 3.4M filings filed January 2022–June 2025. Final count provided in metrics.json upon dataset completion.

Does the dataset contain all possible instances or a sample? This is a complete pull of EDGAR filings within the date range, with the following exceptions:

• Filings that fail parsing after 3+ retry attempts across configurations are excluded (logged as "challenging") — a small minority (<0.1%).
• Auxiliary exhibit attachments (e.g., individual XML information tables) are deduplicated against their parent submissions.

What data does each instance consist of? Plain text in MultiMarkdown format (parsed_md), plus structured metadata fields. No raw bytes, images, or paper PDFs are included.

Is there a label or target? No. The dataset is unlabeled regulatory text. Companion benchmarks EDGAR-OCR and EDGAR-Forecast (released separately) provide labeled evaluation tasks derived from this corpus.

Is any information missing?

• Filings before 2022-01 are not in this release.
• Filings after 2025-06 will be added in future releases.
• Approximately <0.1% of filings within the date range that failed all parsing retries are absent.

Are relationships between instances explicit? Same-issuer relationships can be recovered via the SEC accession number's CIK (Central Index Key) prefix and the SEC-HEADER metadata embedded at the top of each parsed_md. The SEC EDGAR system is the canonical source for these relationships.

Are there recommended data splits? No. SFD-v1 is intended as a single training corpus. Researchers wanting train/eval splits should:

• For temporal evaluation: hold out the latest month (2025-06).
• For company-level evaluation: hold out by CIK.
• For benchmark evaluation: use EDGAR-OCR and EDGAR-Forecast.

Are there errors, sources of noise, or redundancies?

• Parser fidelity: ~99% estimated structural/semantic accuracy. Residual errors include occasional misclassification of fragmented headers, misaligned multi-row tables in pathological HTML, and rare cases where the "Three-Column Hack" merger fails when adjacent columns contain footnote markers that resemble currency modifiers.
• OCR fidelity: Mistral OCR 3 was used for PDF content; small fraction (~1% of corpus) carries has_ocr=True. EDGAR-OCR benchmark measures fidelity at ~75%.
• Filer-level errors: SFD does NOT correct factual or accounting errors in the source filings.
• Duplicates: Some filings re-submit prior content as exhibits or amendments; deduplication by md5 is left to the consumer.

Is the dataset self-contained? Yes. Each row's parsed_md is self-contained and includes prepended SEC-HEADER metadata.

Does the dataset contain confidential data? No. All filings are public regulatory disclosures.

Does the dataset contain offensive, insulting, threatening, or anxiety-inducing content? No, the corpus is corporate regulatory disclosures. Trace amounts of legal proceeding text or risk-factor language could in theory describe sensitive events but are factual and public.

Does the dataset relate to people? Some filings (e.g., Form 4, Form 13D/G) name natural persons in their capacity as insiders, beneficial owners, or filers. These individuals are public regulatory reporters; their personal information appears only in the form required by SEC rules.

Does the dataset identify any subpopulations or contain protected categories? No.

Collection Process

How was the data acquired? By downloading filings via the SEC EDGAR public archive (https://www.sec.gov/edgar). All access conformed to SEC rate-limiting and User-Agent guidance.

What mechanisms or procedures were used to collect the data? Custom Python downloader respecting SEC's 10-requests-per-second guidance, with periodic master-index reconciliation.

If the dataset is a sample, what was the sampling strategy? Not a sample — exhaustive over the date range.

Who was involved in the data collection? The authors and engineering team within the Anonymous Organization.

Over what timeframe was the data collected? Filings cover 2022-01 through 2025-06. Collection (download + parse) was performed in 2024–2026 on shared HPC infrastructure.

Were any ethical review processes conducted? The data is publicly filed regulatory disclosures, so no IRB review was required. The authors confirmed compliance with SEC's terms of service and data-access guidance.

Preprocessing / Cleaning / Labeling

Was any preprocessing done? Yes. The full SFD parsing pipeline:

1. SGML-header parsing → routing by CONFORMED SUBMISSION TYPE.
2. Format-specific reconstruction (HTML / XML / plaintext / SGML / PDF).
3. Numerical normalization (decomma).
4. Non-semantic artifact removal (orphan page numbers, etc.).
5. Metadata prepending (CIK, SIC, form type, period of report).

Was the raw data saved alongside? Raw filings are not redistributed in this release but are available canonically from SEC EDGAR. Rows include accession, file_stem, and md5 to support reproducibility.

Is the preprocessing software available? The SFD parser source will be released alongside paper acceptance. See paper §3 for full methodological detail.

Uses

Has the dataset been used for any tasks already?

• Long-context language model pretraining experiments by the dataset authors.
• Construction of EDGAR-OCR and EDGAR-Forecast benchmarks.

What other tasks could the dataset be used for?

• Financial named-entity / relation extraction (alignable with XBRL).
• Retrieval-augmented generation over filings.
• Compliance and audit automation.
• Long-context modeling research (10K-1M+ tokens per document).
• Table understanding research (tables faithfully preserved in MMD).

Is there anything about the composition of the dataset that might impact future uses?

• Recent-year skew may bias models toward post-pandemic disclosure language.
• The U.S.-centric scope limits cross-jurisdictional finance research.
• The CC BY-NC license restricts commercial use of the parsed corpus.

Are there tasks for which the dataset should not be used?

• Investment decisions or trading: SFD is a research dataset; do not use as a financial advisor.
• Identifying or harming individual reporting persons: while names are present, using the corpus to target individuals is contrary to its research intent.
• Commercial product training: forbidden by CC BY-NC 4.0.

Distribution

Will the dataset be distributed to third parties? Yes — publicly via HuggingFace Datasets at https://huggingface.co/datasets/mikedd/EDGAR_FILINGS_DATASET.

How will the dataset be distributed? Parquet shards on HuggingFace, accessible via datasets.load_dataset(...).

When will the dataset be distributed? v1: with the NeurIPS 2026 E&D Track submission. Subject to date of camera-ready release.

Will the dataset be distributed under a copyright or other IP license? Yes — CC BY-NC 4.0. See LICENSE notice in README.

Have any third parties imposed IP-based or other restrictions?

• Mistral AI Terms of Service govern OCR-derived content within the corpus (~1% of rows have has_ocr=True). Mistral permits downstream redistribution of OCR outputs.
• Filer-supplied content is U.S. Government public domain (filings are public records under SEC rules).

Do any export controls or other regulatory restrictions apply? No.

Maintenance

Who is supporting/maintaining the dataset? The Anonymous Organization.

How can the owner/curator be contacted? Anonymous during double-blind review. HuggingFace repo discussions for issue reports.

Is there an erratum? None at v1 release; future errata will be appended to this datasheet and tagged in the HF repo.

Will the dataset be updated? Yes — see versioning plan in README. Coverage will be extended both backward (1994–2021) and forward (2025-07+).

If the dataset relates to people, are there limits on retention of data relating to specific individuals? N/A — all data is publicly filed regulatory information with no expectation of privacy.

Will older versions be supported/hosted? Yes — version tags will remain accessible via HF dataset revisions.

Mechanisms for contribution? External contributions of additional filing-type parsers, corrections to specific filings, or extension to non-U.S. regulatory archives can be proposed via HF discussions or by contacting the authors.