---
license: cc-by-nc-4.0
language:
  - en
size_categories:
  - 1M<n<10M
task_categories:
  - text-generation
  - question-answering
  - table-question-answering
  - text2text-generation
pretty_name: SFD - SEC Filings Dataset (v1)
tags:
  - finance
  - sec
  - edgar
  - financial-disclosure
  - long-context
  - regulatory
  - 10-K
  - 10-Q
  - 8-K
  - tables
  - multimarkdown
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/parsed/*.parquet
---

# 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](https://fletcher.github.io/MultiMarkdown-6/) — 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

```python
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):

```python
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:

```python
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 `&nbsp;` 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

```bibtex
@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](https://creativecommons.org/licenses/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](https://creativecommons.org/licenses/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](https://mistral.ai/terms/) 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