Datasetpublic

zorynthiq/zoryntiq-sec-filings

Zoryntiq SEC Filings Dataset A clean, LLM-ready dataset of SEC EDGAR filings from recently IPO'd and pre-IPO companies. Dataset Summary 5,179 text chunks extracted from 261 high-signal SEC filings (S-1, 10-K, 10-Q, 8-K, DRS, and more). Each chunk is ~1,500 words with 150-word overlap, cleaned and normalized for LLM training and financial NLP tasks. What's inside Registration statements (S-1, S-1/A, DRS) — full IPO prospectuses including business… See the full description on the dataset page: https://huggingface.co/datasets/zorynthiq/zoryntiq-sec-filings.

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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Dataset Card

Zoryntiq SEC Filings Dataset

A clean, LLM-ready dataset of SEC EDGAR filings from recently IPO'd and pre-IPO companies.

Dataset Summary

5,179 text chunks extracted from 261 high-signal SEC filings (S-1, 10-K, 10-Q, 8-K, DRS, and more). Each chunk is ~1,500 words with 150-word overlap, cleaned and normalized for LLM training and financial NLP tasks.

What's inside

—Registration statements (S-1, S-1/A, DRS) — full IPO prospectuses including business descriptions, risk factors, financials, and use of proceeds
—Annual & quarterly reports (10-K, 10-Q) — MD&A, financial statements, risk disclosures
—Current reports (8-K) — material events, press releases, earnings announcements
—Proxy statements (DEF 14A) — governance, executive compensation
—SEC correspondence (CORRESP) — SEC review letters and company responses

Data fields

Field	Type	Description
`id`	string	Stable unique ID (SHA-256 of accession + filename + chunk index)
`source`	string	Always `"sec_edgar"`
`filing_type`	string	SEC form type (S-1, 10-K, 8-K, …)
`document_type`	string	Specific document or exhibit type
`document_desc`	string	Human-readable document description
`company_name`	string	Full legal company name
`ticker`	string or null	Stock ticker symbol
`cik`	string	SEC Central Index Key
`date_filed`	string	Filing date (YYYY-MM-DD)
`exchange`	string or null	Exchange (NASDAQ, NYSE, …)
`sic`	string	SIC industry code
`sic_description`	string	SIC industry description
`accession_number`	string	SEC accession number
`url`	string	Direct URL to the source document on SEC EDGAR
`chunk_index`	int	0-based chunk position within the document
`total_chunks`	int	Total chunks in this document
`word_count`	int	Word count of this chunk
`char_count`	int	Character count of this chunk
`text`	string	Clean plain text (LLM-ready)
`financials`	dict or null	Key XBRL financial metrics (primary doc chunk 0 only)

`financials` sub-fields (when present)

Field	Description
`revenues_usd`	Total revenues
`net_income_loss_usd`	Net income or loss
`assets_usd`	Total assets
`liabilities_usd`	Total liabilities
`equity_usd`	Stockholders' equity
`cash_usd`	Cash and cash equivalents
`operating_income_usd`	Operating income or loss
`gross_profit_usd`	Gross profit
`eps_basic`	Basic earnings per share
`eps_diluted`	Diluted earnings per share
`shares_outstanding`	Common shares outstanding
`period_end`	Reporting period end date (YYYY-MM-DD)

Cleaning pipeline

1.Form-type filter — high-signal forms only (S-1, 10-K, 10-Q, 8-K, DEF 14A, DRS, CORRESP, …)
2.Complete-submission filter — skip the monolithic EDGAR .txt wrapper file
3.Exhibit filter — skip EX-31/32/23 boilerplate certifications and binary graphics
4.SGML envelope strip — remove <DOCUMENT>…<TEXT> wrapper
5.HTML → plain text — tables preserved as tab-separated rows
6.Normalisation — control chars, junk lines, page numbers removed
7.Min-word threshold — chunks under 200 words dropped
8.Language filter — English only
9.Content deduplication — MD5 hash; exact duplicates removed
10.Chunking — paragraph-aware ~1,500 words with 150-word overlap

Usage

python

from datasets import load_dataset

ds = load_dataset("zoryntiq/zoryntiq-sec-filings")

# All S-1 registration statement chunks
s1 = ds["train"].filter(lambda x: x["filing_type"] in ["S-1", "S-1/A"])

# Only chunks with financial data
with_financials = ds["train"].filter(lambda x: x["financials"] is not None)

Source

All data sourced from SEC EDGAR, the U.S. Securities and Exchange Commission's public filing system. EDGAR data is in the public domain.

Citation

@dataset{zoryntiq_sec_filings_2026,
  title   = {Zoryntiq SEC Filings},
  author  = {Zoryntiq},
  year    = {2026},
  url     = {https://huggingface.co/datasets/zoryntiq/zoryntiq-sec-filings},
  license = {Apache 2.0}
}