| --- |
| 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 |
