Update README.md
#1
by sefd-anonymous - opened
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- README.md +6 -20
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- data/parsed/sfd-v1-2022-02.parquet +2 -2
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README.md
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- text-generation
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- question-answering
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- table-question-answering
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pretty_name: SFD - SEC Filings Dataset (v1)
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tags:
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- finance
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**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.
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This release covers filings from **January 2022 through June 2025** (~3.4M filings), produced by the
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> *The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data.* Anonymous Authors (NeurIPS 2026 Evaluations & Datasets Track, under review).
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- **Source formats handled:** HTML (\~62%), XML (\~18%), plaintext (\~18%), SGML (\~2%), PDF-via-OCR (\~1%) — see paper §3.
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- **Filings:** ~3.4M parsed `.md` documents across 350+ filing types (10-K, 10-Q, 8-K, Form 4, N-PORT, 13F, 485BPOS, ABS-EE, …).
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- **License:** CC-BY-NC-4.0 (parsed). Underlying SEC filings are U.S. Government public domain.
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- **Storage:** Parquet shards with zstd-15 compression, one shard per (year, month).
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## Schema
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```python
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from datasets import load_dataset
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ds = load_dataset("
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for row in ds.take(1):
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print(row["accession"], row["year"], row["month"], len(row["parsed_md"]))
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```
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For a full local materialization (~50 GB on disk):
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```python
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ds = load_dataset("
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ds = ds.filter(lambda r: r["year"] == 2024) # or by month, source_format, etc.
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```
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url = f"https://www.sec.gov/Archives/edgar/data/{int(acc.split('-')[0])}/{acc_clean}/"
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```
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## Sample subset (for reviewer inspection)
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Because the full corpus exceeds 4 GB, a small sample is provided at
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`sample/sfd-v1-sample.parquet` to allow quick inspection of data quality without
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materializing the full dataset.
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- **Path:** `sample/sfd-v1-sample.parquet`
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- **Sampling methodology:** stratified by year — first/last representative month
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of each year in the release (2022-01, 2023-01, 2024-01, 2025-01, 2025-06),
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with the first 25 rows from each, sorted by accession. This surfaces a mix of
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source formats (HTML / XML / SGML / PDF-OCR) and form types (Form 4, 10-Q,
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8-K, NPORT-P, etc.) representative of the full corpus.
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- **Loading:** `pq.read_table("sample/sfd-v1-sample.parquet")` with the same
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schema as the full dataset.
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-
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## Methodology summary
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SFD treats filings as 2-D rendered grids rather than DOM trees:
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## Citation
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```bibtex
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@inproceedings{
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title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data},
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author={Anonymous Authors},
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booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations \& Datasets Track},
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- text-generation
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- question-answering
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- table-question-answering
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- text2text-generation
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pretty_name: SFD - SEC Filings Dataset (v1)
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tags:
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- finance
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**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.
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+
This release covers filings from **January 2022 through June 2025** (~3.4M filings), produced by the EFD parser described in:
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> *The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data.* Anonymous Authors (NeurIPS 2026 Evaluations & Datasets Track, under review).
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- **Source formats handled:** HTML (\~62%), XML (\~18%), plaintext (\~18%), SGML (\~2%), PDF-via-OCR (\~1%) — see paper §3.
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- **Filings:** ~3.4M parsed `.md` documents across 350+ filing types (10-K, 10-Q, 8-K, Form 4, N-PORT, 13F, 485BPOS, ABS-EE, …).
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- **License:** CC-BY-NC-4.0 (parsed). Underlying SEC filings are U.S. Government public domain.
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- **Storage:** Parquet shards with zstd-15 compression, one shard per (year, month).
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## Schema
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```python
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from datasets import load_dataset
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ds = load_dataset("mikedd/EDGAR_FILINGS_DATASET", split="train", streaming=True)
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for row in ds.take(1):
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print(row["accession"], row["year"], row["month"], len(row["parsed_md"]))
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```
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For a full local materialization (~50 GB on disk):
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```python
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ds = load_dataset("mikedd/EDGAR_FILINGS_DATASET", split="train")
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ds = ds.filter(lambda r: r["year"] == 2024) # or by month, source_format, etc.
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```
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url = f"https://www.sec.gov/Archives/edgar/data/{int(acc.split('-')[0])}/{acc_clean}/"
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```
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## Methodology summary
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SFD treats filings as 2-D rendered grids rather than DOM trees:
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## Citation
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```bibtex
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@inproceedings{bettencourt2026sfd,
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title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data},
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author={Anonymous Authors},
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booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations \& Datasets Track},
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croissant.json
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"conformsTo": "dct:conformsTo",
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"cr": "http://mlcommons.org/croissant/",
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"rai": "http://mlcommons.org/croissant/RAI/",
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-
"prov": "http://www.w3.org/ns/prov#",
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"data": {
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"@id": "cr:data",
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"@type": "@json"
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},
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"@type": "sc:Dataset",
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"name": "SFD",
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-
"description": "The
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"conformsTo": "http://mlcommons.org/croissant/1.0",
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"license": "https://creativecommons.org/licenses/by-nc/4.0/",
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"url": "https://huggingface.co/datasets/
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"version": "1.0.0",
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"datePublished": "2026-05-06",
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"creator": [
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{
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"@type": "Person",
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"name": "
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"affiliation": {
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"@type": "Organization",
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"name": "
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}
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}
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],
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"publisher": {
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"@type": "Organization",
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"name": "
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},
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"citeAs": "@inproceedings{
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"keywords": [
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"finance",
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"sec",
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"@id": "repo",
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"name": "repo",
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"description": "Hugging Face dataset git repository hosting SFD-v1.",
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"contentUrl": "https://huggingface.co/datasets/
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"encodingFormat": "git+https",
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"sha256": "https://github.com/mlcommons/croissant/issues/80"
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},
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"rai:annotationsPerItem": "0 (no human annotation)",
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"rai:annotatorDemographics": "N/A - automated parsing only.",
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"rai:dataSocialImpact": "Lowers the barrier to studying U.S. corporate and financial disclosure at scale, supports auditing of LLMs on real-world regulatory text, and provides an open alternative to closed financial corpora.",
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"rai:dataBiases": "Coverage is U.S.-centric and skewed toward issuers active in U.S. capital markets. Filing-type token distribution is highly non-uniform (the top 5 form types account for ~35% of tokens). Recent years are over-represented relative to the full EDGAR archive in this v1 release."
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"rai:hasSyntheticData": false,
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"prov:wasDerivedFrom": [
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{
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"@type": "prov:Entity",
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"name": "U.S. SEC EDGAR public archive",
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"url": "https://www.sec.gov/edgar"
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}
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],
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"prov:wasGeneratedBy": [
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{
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"@type": "prov:Activity",
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"name": "SFD parser pipeline",
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"description": "Format-routed parsing pipeline that reconstructs SEC filings from heterogeneous HTML/XML/plaintext/SGML/PDF source formats into layout-faithful MultiMarkdown. Source release will accompany paper acceptance."
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},
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{
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"@type": "prov:Activity",
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"name": "Mistral OCR 3 (PDF subset only)",
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"description": "Used to OCR a small minority (~1% of corpus, has_ocr=true) of PDF-only filings. Outputs subject to Mistral AI Terms of Service.",
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"url": "https://mistral.ai/terms/"
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}
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]
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}
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"conformsTo": "dct:conformsTo",
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"cr": "http://mlcommons.org/croissant/",
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"rai": "http://mlcommons.org/croissant/RAI/",
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"data": {
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"@id": "cr:data",
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"@type": "@json"
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},
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"@type": "sc:Dataset",
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"name": "SFD",
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"description": "The Stanford EDGAR Filings Dataset (SFD-v1) is an open, layout-faithful reconstruction of U.S. SEC EDGAR filings into token-efficient MultiMarkdown. This release covers ~3.4M filings filed between January 2022 and June 2025, parsed from heterogeneous HTML, XML, plaintext, SGML, and PDF source formats. Tables are reconstructed using MultiMarkdown span notation (`||` for column spans, `^^` for row spans), preserving merged cells, multi-row headers, and indentation hierarchies that standard text extraction destroys. SFD-v1 supports long-context language modeling, financial reasoning, document understanding, and evaluation. The full SFD corpus is estimated at ~500B tokens across ~18.4M filings (1994-present); this release is a public snapshot of the most recent 4.5 years.",
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"conformsTo": "http://mlcommons.org/croissant/1.0",
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"license": "https://creativecommons.org/licenses/by-nc/4.0/",
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"url": "https://huggingface.co/datasets/mikedd/EDGAR_FILINGS_DATASET",
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"version": "1.0.0",
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"datePublished": "2026-05-06",
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"creator": [
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{
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"@type": "Person",
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"name": "Nick Bettencourt",
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"affiliation": {
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"@type": "Organization",
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"name": "University of California, Los Angeles, Department of Applied Statistics and Data Science"
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},
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"email": "nbetts@g.ucla.edu"
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},
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{
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"@type": "Person",
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"name": "Xiaowei Ding",
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"affiliation": {
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"@type": "Organization",
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"name": "Stanford University, Advanced Financial Technologies Lab"
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}
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},
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{
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"@type": "Person",
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"name": "Kay Giesecke",
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"affiliation": {
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"@type": "Organization",
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"name": "Stanford University, Advanced Financial Technologies Lab"
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},
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"email": "giesecke@stanford.edu"
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}
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],
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"publisher": {
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"@type": "Organization",
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"name": "Stanford Advanced Financial Technologies Lab"
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},
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"citeAs": "@inproceedings{bettencourt2026sfd, title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data}, author={Bettencourt, Nick and Ding, Xiaowei and Giesecke, Kay}, booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track}, year={2026}}",
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"keywords": [
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"finance",
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"sec",
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"@id": "repo",
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"name": "repo",
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"description": "Hugging Face dataset git repository hosting SFD-v1.",
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"contentUrl": "https://huggingface.co/datasets/mikedd/EDGAR_FILINGS_DATASET",
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"encodingFormat": "git+https",
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"sha256": "https://github.com/mlcommons/croissant/issues/80"
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},
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"rai:annotationsPerItem": "0 (no human annotation)",
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"rai:annotatorDemographics": "N/A - automated parsing only.",
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"rai:dataSocialImpact": "Lowers the barrier to studying U.S. corporate and financial disclosure at scale, supports auditing of LLMs on real-world regulatory text, and provides an open alternative to closed financial corpora.",
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+
"rai:dataBiases": "Coverage is U.S.-centric and skewed toward issuers active in U.S. capital markets. Filing-type token distribution is highly non-uniform (the top 5 form types account for ~35% of tokens). Recent years are over-represented relative to the full EDGAR archive in this v1 release."
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}
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data/parsed/sfd-v1-2022-02.parquet
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version https://git-lfs.github.com/spec/v1
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size
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version https://git-lfs.github.com/spec/v1
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