sfd-anonymous/sfd-v1

SFD: SEC Filings Dataset (v1)

SFD-v1 is an open, layout-faithful reconstruction of U.S. Securities and Exchange Commission (SEC) EDGAR filings into token-efficient MultiMarkdown (MMD), targeted at long-context language modeling, financial reasoning, document understanding, and evaluation.

This release covers filings from January 2022 through June 2025 (~3.8M filings; ~152B Qwen3-1.7B tokens).

The full SFD corpus is estimated at ~500B tokens across ~18.4M filings (1994–present); this release is a public snapshot focused on the most recent four-and-a-half years.

Key facts

• Format: MultiMarkdown — preserves merged-cell tables (|| colspan, ^^ rowspan), indentation, and visual hierarchy that standard text extraction destroys.
• Source formats handled: HTML (~72.6%), XML (~24.1%), plaintext (~1.6%), SGML (~0.9%), PDF-via-OCR (~0.8%) — see paper §3.
• Filings: ~3.8M parsed .md documents across 350+ filing types (10-K, 10-Q, 8-K, Form 4, N-PORT, 13F, 485BPOS, ABS-EE, …).
• Tokens: ~152B Qwen3-1.7B tokens.
• License: CC-BY-NC-4.0 (parsed). Underlying SEC filings are U.S. Government public domain.
• Storage: Parquet shards with zstd-15 compression, one shard per (year, month).

Schema

Each row is one parsed filing.

field	type	description
accession	string	SEC accession number (e.g. 0000320193-24-000123)
file_stem	string	Stem of the original submission file
year	int16	Filing year
month	int8	Filing month (1–12)
parsed_md	string	MultiMarkdown reconstruction of the filing
char_count	int64	Character count of parsed_md
md5	string	MD5 of parsed_md (UTF-8)
has_ocr	bool	True if any portion required Mistral OCR
source_format	string	Primary source format (html, xml, plaintext, sgml, pdf)

Loading

from datasets import load_dataset

ds = load_dataset("sfd-anonymous/sfd-v1", split="train", streaming=True)
for row in ds.take(1):
    print(row["accession"], row["year"], row["month"], len(row["parsed_md"]))

For a full local materialization (~50 GB on disk):

ds = load_dataset("sfd-anonymous/sfd-v1", split="train")
ds = ds.filter(lambda r: r["year"] == 2024)  # or by month, source_format, etc.

To recover the canonical SEC EDGAR URL for any row:

acc = row["accession"]                              # e.g. "0000320193-24-000123"
acc_clean = acc.replace("-", "")
url = f"https://www.sec.gov/Archives/edgar/data/{int(acc.split('-')[0])}/{acc_clean}/"

Methodology summary

SFD treats filings as 2-D rendered grids rather than DOM trees:

• HTML: Reconstructs the visual coordinate system; collapses the "Three-Column Hack" ($ / value / closing-paren split into separate cells); coalesces fragmented multi-row headers using border-* and margin-* cues; preserves indentation hierarchy by binning CSS units into discrete   levels.
• XML: Routes 33 specialized schemas (Forms 3/4/5, 13F, N-PORT, N-CEN, etc.) through schema-aware emitters that reconstruct human-readable disclosures from field hierarchies.
• Plaintext / SGML: Wraps fixed-width legacy filings in code fences to preserve column alignment; collapses 3+ blank lines to 2.
• PDFs: Run through Mistral OCR 3 in 10-page batches; HTML table fragments converted to MMD; near-blank pages skipped via pixel-variance filter.

Every row is prepended with <SEC-HEADER>-derived metadata (CIK, SIC, filing type, period of report, etc.) so each filing is self-contained.

See paper §3 for full details.

Companion benchmarks

Two evaluation benchmarks are derived from SFD and reported alongside this dataset:

• EDGAR-OCR — 300 hand-selected SEC tables, synthetically perturbed for contamination resistance, scored by adjusted recall over (content × placement × inline formatting).

• EDGAR-Forecast — 50 companies × 5 numeric targets each (250 total) drawn from 2026 10-Q filings, evaluated agentically with prior 5-year filing history visible.

• EDGAR-OCR: https://huggingface.co/datasets/sfd-anonymous/edgar-ocr-benchmark

• EDGAR-Forecast: https://huggingface.co/datasets/sfd-anonymous/edgar-forecast-benchmark

Citation

@inproceedings{anonymous2026sfd,
  title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data},
  author={Anonymous Authors},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations \& Datasets Track},
  year={2026}
}

License & terms

• Parsed corpus (this release): CC BY-NC 4.0 — non-commercial use only with attribution. Derivative works must keep this notice.
• Underlying raw filings: U.S. Government public domain, available canonically from the SEC EDGAR system at https://www.sec.gov/edgar. Redistribution of raw filings under CC BY 4.0 attribution-style metadata is permitted.
• Mistral OCR outputs (the small PDF subset, has_ocr == True) are subject to the Mistral AI Terms of Service at the time of generation; downstream redistribution within this CC BY-NC 4.0 corpus is permitted.

Ethics, privacy, limitations

• All filings are public regulatory disclosures with no expectation of privacy.
• The dataset preserves filer-supplied content verbatim; SFD does not correct factual or accounting errors in the source filings.
• The MMD reconstruction is high-fidelity but not perfect; estimated ~99% structural/semantic accuracy. A small minority of filings (notably highly visual exhibits with low OCR-recoverable content) may have degraded representation.
• Token counts reflect the Qwen3-1.7B tokenizer; other tokenizers will differ.

Provenance & versions

• v1 (this release): 2022-01 → 2025-06, parsed by SFD pipeline rev sec_parser.
• Future releases will extend coverage to 1994–2021 and incrementally to 2025-07+.

Contact

• Anonymous Authors