---
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

```json
{
  "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

```python
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](https://www.sec.gov/edgar) DEF 14A filings using:
- [MinerU](https://github.com/opendatalab/MinerU) for PDF table extraction
- [Qwen3-VL-32B](https://huggingface.co/Qwen/Qwen3-VL-32B-Instruct) for classification and extraction

## GitHub

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