Datasets:

kabasshouse
/

epstein-data

license: cc-by-4.0
task_categories:
  - text-classification
  - question-answering
  - feature-extraction
language:
  - en
tags:
  - legal
  - ocr
  - documents
  - foia
  - knowledge-graph
  - financial
size_categories:
  - 1M<n<10M
pretty_name: Epstein DOJ Document Archive (OCR + Structured Data)

Epstein DOJ Document Archive v2

1.42 million OCR'd documents from the Department of Justice Jeffrey Epstein document release, with structured entity extraction, vector embeddings, financial transactions, communication records, and a forensic audit trail.

Frontend: epstein.academy

What's New in v2

10.6M entities (up from 8.5M) — expanded NER extraction
2.1M chunk embeddings (up from 1.96M) — more documents embedded
49,770 financial transactions — credit card and bank records (DeepSeek extraction)
3,038 derived events — reconstructed calendar, travel, and financial timeline
5,766 curated gold documents — expert-annotated research catalog across 5 subjects
143 investigative records — law enforcement reports and evidence logs
128 communication records — phone call and CDR data
604 DS10 files upgraded from Tesseract to Gemini OCR

Quick Start

HuggingFace Datasets (streaming)

from datasets import load_dataset

# Stream documents without downloading everything
ds = load_dataset("kabasshouse/epstein-data", "documents", streaming=True)
for doc in ds["train"]:
    print(doc["file_key"], doc["document_type"], len(doc["full_text"] or ""))
    break

DuckDB (direct Parquet queries)

-- Query directly from HuggingFace without downloading
SELECT file_key, document_type, date, char_count
FROM 'hf://datasets/kabasshouse/epstein-data/data/documents/*.parquet'
WHERE document_type = 'Email'
  AND date LIKE '2015%'
ORDER BY date
LIMIT 20;

-- Financial transactions
SELECT transaction_date, amount, merchant_name, cardholder
FROM 'hf://datasets/kabasshouse/epstein-data/data/financial_transactions/*.parquet'
WHERE cardholder LIKE '%EPSTEIN%'
  AND amount > 1000
ORDER BY amount DESC
LIMIT 20;

-- Curated gold documents
SELECT file_key, subject, tier, headline, key_quote
FROM 'hf://datasets/kabasshouse/epstein-data/data/curated_docs/*.parquet'
WHERE tier = 'NUCLEAR'
ORDER BY subject, doc_date;

Pandas

import pandas as pd

docs = pd.read_parquet("hf://datasets/kabasshouse/epstein-data/data/documents/")
print(f"{len(docs):,} documents")
print(docs.groupby("dataset").size().sort_values(ascending=False))

Data Layers

Core Content

Layer	Rows	Description
`documents`	1,424,673	Full OCR text, document type, date, photo flag
`entities`	10,629,198	Named entities (person, org, location, date, etc.)
`chunks`	2,193,090	~800-token text chunks for RAG
`embeddings_chunk`	2,111,356	768-dim Gemini embeddings per chunk

Knowledge & Analysis

Layer	Rows	Description
`persons`	1,614	Curated person registry (name, aliases, category)
`kg_entities`	467	Knowledge graph nodes
`kg_relationships`	2,198	Knowledge graph edges (traveled_with, associated_with, etc.)
`recovered_redactions`	37,870	ML-recovered text from redacted pages
`curated_docs`	5,766	Expert-annotated gold documents (5 subjects, tiered)

Structured Records (NEW in v2)

Layer	Rows	Description
`financial_transactions`	49,770	Credit card & bank transactions
`derived_events`	3,038	Reconstructed calendar/travel/financial events
`event_participants`	5,751	People linked to derived events
`event_sources`	21,910	Source documents for derived events
`investigative_records`	143	Law enforcement reports & evidence logs
`communication_records`	128	Phone call & CDR records

Provenance

Layer	Rows	Description
`provenance/files`	1,387,775	Per-file processing metadata + SHA-256 checksums
`provenance/audit_log`	3,711,609	Append-only forensic audit trail
`provenance/runs`	123	Pipeline execution records

Datasets

Dataset	Files	Source
DataSet 1	3,158	DOJ EFTA release
DataSet 2	574	DOJ EFTA release
DataSet 3	67	DOJ EFTA release
DataSet 4	152	DOJ EFTA release
DataSet 5	120	DOJ EFTA release
DataSet 6	13	DOJ EFTA release
DataSet 7	17	DOJ EFTA release
DataSet 8	10,595	DOJ EFTA release
DataSet 9	531,279	DOJ EFTA release (community Tesseract OCR)
DataSet 10	503,154	DOJ EFTA release
DataSet 11	331,655	DOJ EFTA release
DataSet 12	152	DOJ EFTA release
FBIVault	22	FBI Vault FOIA release
HouseOversightEstate	4,892	House Oversight Committee

468 unrecoverable failures (corrupt/empty source PDFs). Full failure catalog in release/epstein_problems.json.

OCR Sources

Gemini 2.5 Flash Lite: 856,028 files — structured JSON output with entities, document classification, and metadata
Tesseract (community): 531,279 files — raw text only (DataSet 9, community gap-fill imports)
Upgraded: 1,377 files originally processed with Tesseract, now re-processed with Gemini

Distinguish OCR source via the ocr_source column: NULL = Gemini, 'tesseract-community' = community Tesseract.

Curated Documents

The curated_docs layer contains 5,766 expert-annotated gold documents across 5 investigation subjects:

Subject	Gold Docs	Tiers
Hoffman	1,526	NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING
Gates	2,069	NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING
Summers	739	NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING
Clinton	765	NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING
Black	667	NUCLEAR / CRITICAL / HIGH / MEDIUM / SUPPORTING

Each entry includes: tier, category, date, sender/recipient, headline, key quote, and investigative detail.

Financial Transactions

49,770 clean records extracted from credit card statements and bank records using DeepSeek. Includes:

Transaction date, amount, currency, merchant
Cardholder name (Epstein, Maxwell, Shuliak, etc.)
Flight data (origin, destination, carrier, passenger) for airline purchases
Merchant category classification

31% of raw extractions were quarantined for quality issues and excluded from this release.

License

CC-BY-4.0. Source documents are U.S. government public records.

Citation

@dataset{epstein_archive_2026,
  title={Epstein DOJ Document Archive},
  author={kabasshouse},
  year={2026},
  url={https://huggingface.co/datasets/kabasshouse/epstein-data},
  version={2.0}
}