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  1. README.md +6 -20
  2. croissant.json +26 -30
  3. data/parsed/sfd-v1-2022-02.parquet +2 -2
  4. data/parsed/sfd-v1-2022-03.parquet +2 -2
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  47. data/parsed/sfd-v1-2024-04-part09.parquet +0 -3
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  49. data/parsed/{sfd-v1-2024-02-part03.parquet → sfd-v1-2024-04.parquet} +2 -2
  50. data/parsed/sfd-v1-2024-05-part01.parquet +0 -3
README.md CHANGED
@@ -8,6 +8,7 @@ task_categories:
8
  - text-generation
9
  - question-answering
10
  - table-question-answering
 
11
  pretty_name: SFD - SEC Filings Dataset (v1)
12
  tags:
13
  - finance
@@ -32,7 +33,7 @@ configs:
32
 
33
  **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.
34
 
35
- This release covers filings from **January 2022 through June 2025** (~3.4M filings), produced by the SFD parser described in:
36
 
37
  > *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).
38
 
@@ -44,7 +45,7 @@ The full SFD corpus is estimated at ~500B tokens across ~18.4M filings (1994–p
44
  - **Source formats handled:** HTML (\~62%), XML (\~18%), plaintext (\~18%), SGML (\~2%), PDF-via-OCR (\~1%) — see paper §3.
45
  - **Filings:** ~3.4M parsed `.md` documents across 350+ filing types (10-K, 10-Q, 8-K, Form 4, N-PORT, 13F, 485BPOS, ABS-EE, …).
46
  - **License:** CC-BY-NC-4.0 (parsed). Underlying SEC filings are U.S. Government public domain.
47
- - **Storage:** Parquet shards with zstd-15 compression, one shard per (year, month). Token counts use the Qwen3-1.7B tokenizer. The headline ~152B figure counts the full released `parsed_md` field, including the prepended SEC/SGML filing metadata header. The regulatory body content alone is ~111B tokens; the difference is metadata such as CIK, accession, form type, filing date, period of report, and related SEC header fields.
48
 
49
  ## Schema
50
 
@@ -67,7 +68,7 @@ Each row is one parsed filing.
67
  ```python
68
  from datasets import load_dataset
69
 
70
- ds = load_dataset("anonymous-md/EDGAR_FILINGS_DATASET", split="train", streaming=True)
71
  for row in ds.take(1):
72
  print(row["accession"], row["year"], row["month"], len(row["parsed_md"]))
73
  ```
@@ -75,7 +76,7 @@ for row in ds.take(1):
75
  For a full local materialization (~50 GB on disk):
76
 
77
  ```python
78
- ds = load_dataset("anonymous-md/EDGAR_FILINGS_DATASET", split="train")
79
  ds = ds.filter(lambda r: r["year"] == 2024) # or by month, source_format, etc.
80
  ```
81
 
@@ -87,21 +88,6 @@ acc_clean = acc.replace("-", "")
87
  url = f"https://www.sec.gov/Archives/edgar/data/{int(acc.split('-')[0])}/{acc_clean}/"
88
  ```
89
 
90
- ## Sample subset (for reviewer inspection)
91
-
92
- Because the full corpus exceeds 4 GB, a small sample is provided at
93
- `sample/sfd-v1-sample.parquet` to allow quick inspection of data quality without
94
- materializing the full dataset.
95
-
96
- - **Path:** `sample/sfd-v1-sample.parquet`
97
- - **Sampling methodology:** stratified by year — first/last representative month
98
- of each year in the release (2022-01, 2023-01, 2024-01, 2025-01, 2025-06),
99
- with the first 25 rows from each, sorted by accession. This surfaces a mix of
100
- source formats (HTML / XML / SGML / PDF-OCR) and form types (Form 4, 10-Q,
101
- 8-K, NPORT-P, etc.) representative of the full corpus.
102
- - **Loading:** `pq.read_table("sample/sfd-v1-sample.parquet")` with the same
103
- schema as the full dataset.
104
-
105
  ## Methodology summary
106
 
107
  SFD treats filings as 2-D rendered grids rather than DOM trees:
@@ -127,7 +113,7 @@ Released separately when scoring is finalized.
127
  ## Citation
128
 
129
  ```bibtex
130
- @inproceedings{anonymous2026sfd,
131
  title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data},
132
  author={Anonymous Authors},
133
  booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations \& Datasets Track},
 
8
  - text-generation
9
  - question-answering
10
  - table-question-answering
11
+ - text2text-generation
12
  pretty_name: SFD - SEC Filings Dataset (v1)
13
  tags:
14
  - finance
 
33
 
34
  **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.
35
 
36
+ This release covers filings from **January 2022 through June 2025** (~3.4M filings), produced by the EFD parser described in:
37
 
38
  > *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).
39
 
 
45
  - **Source formats handled:** HTML (\~62%), XML (\~18%), plaintext (\~18%), SGML (\~2%), PDF-via-OCR (\~1%) — see paper §3.
46
  - **Filings:** ~3.4M parsed `.md` documents across 350+ filing types (10-K, 10-Q, 8-K, Form 4, N-PORT, 13F, 485BPOS, ABS-EE, …).
47
  - **License:** CC-BY-NC-4.0 (parsed). Underlying SEC filings are U.S. Government public domain.
48
+ - **Storage:** Parquet shards with zstd-15 compression, one shard per (year, month).
49
 
50
  ## Schema
51
 
 
68
  ```python
69
  from datasets import load_dataset
70
 
71
+ ds = load_dataset("mikedd/EDGAR_FILINGS_DATASET", split="train", streaming=True)
72
  for row in ds.take(1):
73
  print(row["accession"], row["year"], row["month"], len(row["parsed_md"]))
74
  ```
 
76
  For a full local materialization (~50 GB on disk):
77
 
78
  ```python
79
+ ds = load_dataset("mikedd/EDGAR_FILINGS_DATASET", split="train")
80
  ds = ds.filter(lambda r: r["year"] == 2024) # or by month, source_format, etc.
81
  ```
82
 
 
88
  url = f"https://www.sec.gov/Archives/edgar/data/{int(acc.split('-')[0])}/{acc_clean}/"
89
  ```
90
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
91
  ## Methodology summary
92
 
93
  SFD treats filings as 2-D rendered grids rather than DOM trees:
 
113
  ## Citation
114
 
115
  ```bibtex
116
+ @inproceedings{bettencourt2026sfd,
117
  title={The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data},
118
  author={Anonymous Authors},
119
  booktitle={Advances in Neural Information Processing Systems (NeurIPS), Evaluations \& Datasets Track},
croissant.json CHANGED
@@ -7,7 +7,6 @@
7
  "conformsTo": "dct:conformsTo",
8
  "cr": "http://mlcommons.org/croissant/",
9
  "rai": "http://mlcommons.org/croissant/RAI/",
10
- "prov": "http://www.w3.org/ns/prov#",
11
  "data": {
12
  "@id": "cr:data",
13
  "@type": "@json"
@@ -47,27 +46,45 @@
47
  },
48
  "@type": "sc:Dataset",
49
  "name": "SFD",
50
- "description": "The SEC 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.",
51
  "conformsTo": "http://mlcommons.org/croissant/1.0",
52
  "license": "https://creativecommons.org/licenses/by-nc/4.0/",
53
- "url": "https://huggingface.co/datasets/anonymous-md/EDGAR_FILINGS_DATASET",
54
  "version": "1.0.0",
55
  "datePublished": "2026-05-06",
56
  "creator": [
57
  {
58
  "@type": "Person",
59
- "name": "Anonymous Authors",
60
  "affiliation": {
61
  "@type": "Organization",
62
- "name": "Anonymous Organization"
 
 
 
 
 
 
 
 
 
63
  }
 
 
 
 
 
 
 
 
 
64
  }
65
  ],
66
  "publisher": {
67
  "@type": "Organization",
68
- "name": "Anonymous Organization"
69
  },
70
- "citeAs": "@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 and Datasets Track}, year={2026}}",
71
  "keywords": [
72
  "finance",
73
  "sec",
@@ -90,7 +107,7 @@
90
  "@id": "repo",
91
  "name": "repo",
92
  "description": "Hugging Face dataset git repository hosting SFD-v1.",
93
- "contentUrl": "https://huggingface.co/datasets/anonymous-md/EDGAR_FILINGS_DATASET",
94
  "encodingFormat": "git+https",
95
  "sha256": "https://github.com/mlcommons/croissant/issues/80"
96
  },
@@ -223,26 +240,5 @@
223
  "rai:annotationsPerItem": "0 (no human annotation)",
224
  "rai:annotatorDemographics": "N/A - automated parsing only.",
225
  "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.",
226
- "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.",
227
- "rai:hasSyntheticData": false,
228
- "prov:wasDerivedFrom": [
229
- {
230
- "@type": "prov:Entity",
231
- "name": "U.S. SEC EDGAR public archive",
232
- "url": "https://www.sec.gov/edgar"
233
- }
234
- ],
235
- "prov:wasGeneratedBy": [
236
- {
237
- "@type": "prov:Activity",
238
- "name": "SFD parser pipeline",
239
- "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."
240
- },
241
- {
242
- "@type": "prov:Activity",
243
- "name": "Mistral OCR 3 (PDF subset only)",
244
- "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.",
245
- "url": "https://mistral.ai/terms/"
246
- }
247
- ]
248
  }
 
7
  "conformsTo": "dct:conformsTo",
8
  "cr": "http://mlcommons.org/croissant/",
9
  "rai": "http://mlcommons.org/croissant/RAI/",
 
10
  "data": {
11
  "@id": "cr:data",
12
  "@type": "@json"
 
46
  },
47
  "@type": "sc:Dataset",
48
  "name": "SFD",
49
+ "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.",
50
  "conformsTo": "http://mlcommons.org/croissant/1.0",
51
  "license": "https://creativecommons.org/licenses/by-nc/4.0/",
52
+ "url": "https://huggingface.co/datasets/mikedd/EDGAR_FILINGS_DATASET",
53
  "version": "1.0.0",
54
  "datePublished": "2026-05-06",
55
  "creator": [
56
  {
57
  "@type": "Person",
58
+ "name": "Nick Bettencourt",
59
  "affiliation": {
60
  "@type": "Organization",
61
+ "name": "University of California, Los Angeles, Department of Applied Statistics and Data Science"
62
+ },
63
+ "email": "nbetts@g.ucla.edu"
64
+ },
65
+ {
66
+ "@type": "Person",
67
+ "name": "Xiaowei Ding",
68
+ "affiliation": {
69
+ "@type": "Organization",
70
+ "name": "Stanford University, Advanced Financial Technologies Lab"
71
  }
72
+ },
73
+ {
74
+ "@type": "Person",
75
+ "name": "Kay Giesecke",
76
+ "affiliation": {
77
+ "@type": "Organization",
78
+ "name": "Stanford University, Advanced Financial Technologies Lab"
79
+ },
80
+ "email": "giesecke@stanford.edu"
81
  }
82
  ],
83
  "publisher": {
84
  "@type": "Organization",
85
+ "name": "Stanford Advanced Financial Technologies Lab"
86
  },
87
+ "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}}",
88
  "keywords": [
89
  "finance",
90
  "sec",
 
107
  "@id": "repo",
108
  "name": "repo",
109
  "description": "Hugging Face dataset git repository hosting SFD-v1.",
110
+ "contentUrl": "https://huggingface.co/datasets/mikedd/EDGAR_FILINGS_DATASET",
111
  "encodingFormat": "git+https",
112
  "sha256": "https://github.com/mlcommons/croissant/issues/80"
113
  },
 
240
  "rai:annotationsPerItem": "0 (no human annotation)",
241
  "rai:annotatorDemographics": "N/A - automated parsing only.",
242
  "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.",
243
+ "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."
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
244
  }
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