README.md

metadata

license: cc-by-sa-4.0
task_categories:
  - table-question-answering
language:
  - en
tags:
  - finance
  - 10k
  - edgar
size_categories:
  - 1M<n<10M
configs:
  - config_name: column_documentation
    data_files:
      - split: train
        path: data/column_documentation/column_documentation.parquet
  - config_name: company_addresses
    data_files:
      - split: train
        path: data/company_addresses/company_addresses.parquet
  - config_name: column_comments
    data_files:
      - split: train
        path: data/column_comments/column_comments.parquet
  - config_name: sqlite_sequence
    data_files:
      - split: train
        path: data/sqlite_sequence/sqlite_sequence.parquet
  - config_name: table_documentation
    data_files:
      - split: train
        path: data/table_documentation/table_documentation.parquet
  - config_name: companies
    data_files:
      - split: train
        path: data/companies/companies.parquet
  - config_name: filings
    data_files:
      - split: train
        path: data/filings/filings.parquet
  - config_name: financial_facts
    data_files:
      - split: train
        path: data/financial_facts/financial_facts.parquet
  - config_name: company_tickers
    data_files:
      - split: train
        path: data/company_tickers/company_tickers.parquet
  - config_name: table_comments
    data_files:
      - split: train
        path: data/table_comments/table_comments.parquet

DDRBench 10K Financial Database

This dataset contains the structured SQLite database tables used in the DDRBench 10K evaluation task, converted to Parquet format.

Structure

The dataset is split into multiple configurations, each representing a table from the original database:

• companies: Master table of companies (CIK, Name, etc.).
• financial_facts: The core financial data (5M+ rows).
• filings: Metadata about SEC filings.
• company_addresses: Address information.
• company_tickers: Ticker symbol mappings.
• table/column_documentation: Metadata and comments explaining the schema.

Usage

To load a specific table:

from datasets import load_dataset

# Load the 'companies' table
ds_companies = load_dataset("thinkwee/DDRBench_10K", "companies")

# Load the main financial facts
ds_facts = load_dataset("thinkwee/DDRBench_10K", "financial_facts")