GAAP & SEC Compliance Dataset

A comprehensive dataset for financial AI applications

Dataset Overview

This dataset contains 470,151 documents covering US GAAP (Generally Accepted Accounting Principles) standards and SEC (Securities and Exchange Commission) filing requirements. It's designed for training and evaluating AI systems for financial compliance, accounting Q&A, and regulatory analysis.

Key Statistics

Total Documents: 470,151
Average Length: 363 characters
Unique Companies: 6,573
Date Range: 2007-01-31 to 2025-12-01
Dataset Size: ~296MB

Content Distribution

By Source

XBRL: 445,211 (94.7%)
SEC_FILING: 24,935 (5.3%)
GAAP_STANDARD: 5 (0.0%)

By Document Type

tag: 445,211 (94.7%)
financial_data: 24,935 (5.3%)
standard: 5 (0.0%)

By Category (Top 10)

Other: 294,775 (62.7%)
Expenses: 55,303 (11.8%)
Assets: 35,592 (7.6%)
Liabilities: 32,958 (7.0%)
Income: 24,658 (5.2%)
Equity: 19,732 (4.2%)
Revenue: 7,133 (1.5%)

Use Cases

AI Chatbots

Build intelligent assistants for:

GAAP compliance questions
SEC filing analysis
Accounting standard lookup
Financial regulation guidance

Information Retrieval

Power search engines for:

Financial document discovery
Regulatory text mining
Compliance research
Academic studies

Machine Learning

Train models for:

Financial text classification
Accounting Q&A systems
Regulatory NLP tasks
Domain adaptation

Quick Start

Load Dataset

from datasets import load_dataset

# Load full dataset
dataset = load_dataset("aanshshah/gaap-sec-compliance-dataset")

# Or stream for memory efficiency
dataset = load_dataset("aanshshah/gaap-sec-compliance-dataset", streaming=True)

# Access examples
for example in dataset["train"]:
    print(f"Title: {example['metadata']['title']}")
    print(f"Source: {example['metadata']['source']}")
    print(f"Content: {example['content'][:200]}...")
    break

Build RAG System

from transformers import pipeline
from sentence_transformers import SentenceTransformer
import faiss

# Load dataset
docs = load_dataset("aanshshah/gaap-sec-compliance-dataset")["train"]

# Create embeddings
encoder = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = encoder.encode([doc["content"] for doc in docs])

# Build FAISS index
index = faiss.IndexFlatL2(384)
index.add(embeddings)

def search_docs(query, k=5):
    query_vec = encoder.encode([query])
    _, indices = index.search(query_vec, k)
    return [docs[i] for i in indices[0]]

# Example usage
results = search_docs("What is ASC 606?")

Use with LangChain

from langchain.document_loaders import HuggingFaceDatasetLoader
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings

# Load documents
loader = HuggingFaceDatasetLoader(
    path="aanshshah/gaap-sec-compliance-dataset",
    page_content_column="content"
)
documents = loader.load()

# Create vector store
embeddings = HuggingFaceEmbeddings()
vectorstore = FAISS.from_documents(documents, embeddings)

# Query
results = vectorstore.similarity_search("revenue recognition", k=5)

Dataset Structure

Document Schema

Each document contains:

id: Unique identifier
content: Full text content
metadata: Structured information including:
- source: Origin (XBRL, SEC_FILING, GAAP_STANDARD)
- type: Document type (tag, financial_data, standard)
- category: Financial category (Assets, Revenue, etc.)
- code: Standard code (e.g., "ASC 606", "us-gaap:Assets")
- title: Human-readable title
- date: Date in YYYY-MM-DD format
- company: Company name (for SEC filings)

Example Document

{
  "id": "gaap_standard_67a64e72e3390f7e",
  "content": "# ASC 606: Revenue from Contracts with Customers...",
  "metadata": {
    "source": "GAAP_STANDARD",
    "type": "standard",
    "category": "Revenue",
    "code": "ASC 606",
    "title": "ASC 606: Revenue from Contracts with Customers",
    "date": "2025-01-01"
  }
}

Data Creation Process

Sources

XBRL US GAAP Taxonomy (94.7%)
- Complete standardized accounting tags
- Hierarchical relationships preserved
SEC EDGAR Database (5.3%)
- Real company 10-K/10-Q filings
- Quarterly data from 2007-2025
FASB Standards (<0.1%)
- Core GAAP standards (ASC)
- Implementation guidance

Processing Pipeline

Extraction: Parse XBRL, HTML, PDF sources
Standardization: Convert to consistent JSON format
Cleaning: Remove duplicates and invalid entries
Enrichment: Add metadata and categories
Validation: Ensure quality and completeness

Applications in Production

Financial Institutions

Compliance monitoring systems
Risk assessment tools
Regulatory report generation
Audit automation

FinTech Companies

AI-powered accounting assistants
Automated bookkeeping
Financial analysis platforms
Investment research tools

Education & Training

Interactive learning platforms
Professional certification prep
Academic research
Student Q&A systems

Quality & Coverage

Quality Metrics

Deduplicated: No duplicate documents
Validated: All required fields present
Cleaned: Invalid entries removed
Structured: Consistent schema
Current: Up-to-date as of December 2025

Coverage Areas

Complete US GAAP taxonomy
Major public company filings
All accounting categories
Historical and current standards
Multiple filing types (10-K, 10-Q, 8-K)

Legal & Ethics

Data Sources

All data from public sources
No proprietary information
SEC EDGAR publicly available filings
XBRL taxonomy open standard

Use Restrictions

Not for investment advice
Educational/research purposes
Verify critical information with official sources
Comply with applicable regulations

Privacy

No personal identifying information
No material non-public information
Only public company data
Anonymized where appropriate

Updates & Maintenance

Version History

v1.0.0 (December 2025): Initial release with 470K documents

Update Schedule

Quarterly updates planned
New SEC filings added
GAAP standard updates included
Community feedback incorporated

Support & Community

Getting Help

Discussions
Issues
Contact via HuggingFace profile

Contributing

Report data quality issues
Suggest additional sources
Share use cases and applications
Submit improvements

Citation

If you use this dataset in your research or applications, please cite:

@dataset{gaap_sec_compliance_2025,
  title={GAAP & SEC Compliance Dataset},
  author={Shah, Aansh},
  year={2025},
  month={12},
  publisher={HuggingFace},
  url={https://huggingface.co/datasets/aanshshah/gaap-sec-compliance-dataset},
  note={A comprehensive dataset of 470,151 financial documents for AI applications}
}

Acknowledgments

XBRL US for taxonomy data
SEC EDGAR for public filings
FASB for accounting standards
HuggingFace for hosting platform
FlexAI for compute resources

Dataset Statistics

Metric	Value
Documents	470,151
Characters	171,055,320
Companies	6,573
Date Span	6,573 days
Storage	~296MB

Built for the financial AI community

Ready to build the next generation of financial AI? Start with this dataset!

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support