Datasets:

pierjoe
/

execcomp-ai

dataset_info:
  features:
    - name: cik
      dtype: string
    - name: company
      dtype: string
    - name: year
      dtype: int64
    - name: filing_date
      dtype: string
    - name: sic
      dtype: string
    - name: state_of_inc
      dtype: string
    - name: filing_html_index
      dtype: string
    - name: accession_number
      dtype: string
    - name: table_image
      dtype: image
    - name: table_body
      dtype: string
    - name: executives
      dtype: string
  splits:
    - name: train
      num_bytes: 5590480
      num_examples: 53
  download_size: 5127854
  dataset_size: 5590480
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: mit
task_categories:
  - table-to-text
language:
  - en
tags:
  - finance
pretty_name: execcomp

SEC Executive Compensation Dataset

Structured executive compensation data extracted from SEC DEF 14A proxy statements using AI.

Dataset Description

This dataset contains Summary Compensation Tables extracted from SEC filings, with:

Original table images
HTML table structure
Structured JSON with executive compensation details

Fields

Field	Type	Description
`cik`	string	SEC Central Index Key
`company`	string	Company name
`year`	int	Filing year
`filing_date`	string	SEC filing date
`sic`	string	Standard Industrial Classification code
`state_of_inc`	string	State of incorporation
`filing_html_index`	string	Link to SEC filing
`accession_number`	string	SEC accession number
`table_image`	image	Extracted table image
`table_body`	string	HTML table content
`executives`	string	JSON array of executive compensation

Executive Schema

{
  "name": "John Smith",
  "title": "CEO",
  "fiscal_year": 2023,
  "salary": 500000,
  "bonus": 100000,
  "stock_awards": 2000000,
  "option_awards": 500000,
  "non_equity_incentive": 300000,
  "change_in_pension": 50000,
  "other_compensation": 25000,
  "total": 3475000
}

Usage

from datasets import load_dataset

ds = load_dataset("pierjoe/execcomp-ai-sample")

# View first record
print(ds["train"][0])

# Parse executives JSON
import json
execs = json.loads(ds["train"][0]["executives"])

Source

Data extracted from SEC EDGAR DEF 14A filings using:

MinerU for PDF table extraction
Qwen3-VL-32B for classification and extraction

GitHub

https://github.com/pierpierpy/Execcomp-AI.git