Dataset
EDGAR_FILINGS_DATASET_2022_2026H1
EDGAR_FILINGS_DATASET_2022_2026H1 is a Hugging Face dataset that compiles parsed SEC EDGAR filings into a single tabular corpus. It covers roughly 2022 through the first half of 2026 and is distributed as Parquet with about 1.7 million records.
Data preview
Dataset card
What this dataset contains
The dataset aggregates individual EDGAR filings into a unified table, with one row per filing document. Each record carries the originating accession identifier, the original file name stem, and the year and month of the filing, allowing analysts to slice the corpus across companies and time periods. The release sits in the 1M–10M row range and is built primarily for text-mining and time-series workflows rather than raw XBRL analytics.
Structure and fields
Records expose the following columns: accession and file_stem as string identifiers, year as int16 and month as int8 for temporal filtering, parsed_md as a large_string holding the markdown-rendered filing text, char_count as int64 to gauge document length, md5 as a string hash for deduplication, has_ocr as a boolean flag indicating whether OCR was applied, and source_format as a string describing the original filing format. This schema supports efficient filtering by year and month while preserving the underlying filing content for downstream NLP.
How analysts use it
Quantitative researchers can use the parsed_md and char_count fields to build text-based factor models, event studies around specific filing vintages identified via accession, or trend analyses segmented by year and month. Because md5 enables deduplication and has_ocr flags machine-converted documents, the dataset is also suited for cleaning pipelines that compare parsed text quality before downstream modeling.
Documentation & references 1
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SEC EDGAR 10-K & 10-Q financial statements for 2,600+ NASDAQ companies.
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