Add README.md

README.md

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----
-license: cc-by-nc-4.0
----
+---
+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 - Stanford EDGAR 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: Stanford EDGAR 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** (~3.4M filings), produced by the SFD parser described in:
+
+> *The SEC Filings Dataset: Reconstructing U.S. Corporate and Financial Disclosures into Layout-Faithful and Token-Efficient Pretraining Data.* Bettencourt, Ding, Giesecke (NeurIPS 2026 Evaluations & Datasets Track, under review).
+
+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 (~62%), XML (~18%), plaintext (~18%), SGML (~2%), PDF-via-OCR (~1%) — see paper §3.
+- **Filings:** ~3.4M parsed `.md` documents across 350+ filing types (10-K, 10-Q, 8-K, Form 4, N-PORT, 13F, 485BPOS, ABS-EE, …).
+- **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("mikedd/EDGAR_FILINGS_DATASET", 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("mikedd/EDGAR_FILINGS_DATASET", 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.
+
+Released separately when scoring is finalized.
+
+## Citation
+
+```bibtex
+@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 \& 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_v65`.
+- Future releases will extend coverage to 1994–2021 and incrementally to 2025-07+.
+
+## Contact
+
+- Nick Bettencourt — `nbetts@g.ucla.edu`
+- Xiaowei Ding — Stanford Advanced Financial Technologies Lab
+- Kay Giesecke — `giesecke@stanford.edu`