Dataset
EDGAR_FILINGS_DATASET_2016_2021
EDGAR_FILINGS_DATASET_2016_2021 is a Hugging Face mirror of parsed SEC EDGAR filings spanning 2016 through 2021, distributed as a single train split of about 6 million rows in Parquet format. It is published by anonymous-md with an unstated license.
Data preview
Dataset card
What this dataset contains
The dataset holds parsed Markdown versions of SEC EDGAR filing documents along with basic provenance metadata. Each row corresponds to an individual filing component, identified by an accession number and file_stem, and tagged with a filing year and month between 2016 and 2021. A boolean has_ocr flag indicates whether the source required optical character recognition, and md5 hashes are provided for file integrity checks.
Structure and fields
Records are tabular with nine fields: accession and file_stem as string identifiers, year as int16, month as int8, parsed_md as a large_string holding the rendered filing text, char_count as int64 capturing document length, md5 as a string hash, has_ocr as a bool, and source_format as a string for the originating file type. The single train split contains 6,043,431 rows. The dataset is tagged for use with the datasets, dask, polars, and mlcroissant libraries.
How analysts use it
Because every row carries parsed Markdown from a public SEC filing, the corpus is suited to NLP tasks such as risk-factor extraction, MD&A summarization, and event detection across 10-K, 10-Q, and 8-K documents. Investment analysts can use the year and month fields to slice filings by reporting period and join to market data for event-study or earnings-surprise backtests. The md5 and has_ocr columns support deduplication and quality filtering before any 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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