metadata

license: cc-by-4.0
task_categories:
  - image-text-to-text
  - image-feature-extraction
language:
  - en
tags:
  - pdf
  - ocr
  - legal
  - government
size_categories:
  - 100K<n<1M
dataset_info:
  - config_name: index
    features:
      - name: filename
        dtype: string
      - name: filepath
        dtype: string
      - name: broken_pdf
        dtype: bool
      - name: num_pages
        dtype: float64
      - name: created_date
        dtype: string
      - name: modified_date
        dtype: string
      - name: title
        dtype: string
      - name: author
        dtype: string
      - name: subject
        dtype: string
      - name: file_size_mb
        dtype: float64
      - name: error_message
        dtype: string
    splits:
      - name: train
        num_bytes: 39695484
        num_examples: 229917
    download_size: 19387703
    dataset_size: 39695484
  - config_name: sample
    features:
      - name: pdf
        dtype: pdf
      - name: num_pages
        dtype: float64
      - name: created_date
        dtype: string
      - name: modified_date
        dtype: string
      - name: title
        dtype: string
      - name: author
        dtype: string
      - name: subject
        dtype: string
      - name: file_size_mb
        dtype: float64
      - name: broken_pdf
        dtype: bool
      - name: error_message
        dtype: string
    splits:
      - name: train
        num_bytes: 879832
        num_examples: 5000
    download_size: 400528
    dataset_size: 879832
configs:
  - config_name: index
    data_files:
      - split: train
        path: index/train-*
  - config_name: sample
    data_files:
      - split: train
        path: sample/train-*

govdocs1: source PDF files

This is ~220,000 open-access PDF documents (about 6.6M pages) from the dataset govdocs1. It wants to be OCR'd.

Uploaded as tar file pieces of ~10 GiB each due to size/file count limits with an index.csv covering details
5,000 randomly sampled PDFs are available unarchived in sample/. Hugging Face supports previewing these in-browser, for example this one

Recovering the data

Download the data/ directory (with huggingface-cli download or similar) extract the tar pieces:

cat data_pdfs_part.tar.* | tar -xf - && rm data_pdfs_part.tar.*

processing details

duplicates

exact duplicate PDFs were removed with jdupes. See the log file for details.

By the numbers

Based on the index.csv

Dataset Overview

Metric	Value	Percentage
Total Documents	229,917	100%
Successfully Processed	229,824	99.96%
Broken/Corrupted	93	0.04%
Unique Filenames	229,917	100%

Document Structure

Page Count Distribution

Pages	Count	Percentage
2 pages	21,887	9.5%
1 page	19,282	8.4%
4 pages	14,640	6.4%
3 pages	12,861	5.6%
6 pages	9,770	4.3%

Statistic	Value
Range	1 - 3,200 pages
Mean	27.8 pages
Median	10 pages
Standard Deviation	67.9 pages

File Size Distribution

Size (MB)	Count	Percentage
0.02	13,427	5.8%
0.03	12,142	5.3%
0.04	12,085	5.3%
0.05	11,850	5.2%
0.01	9,929	4.3%

Statistic	Value
Range	0 - 68.83 MB
Mean	0.565 MB
Median	0.15 MB
Standard Deviation	1.134 MB

Metadata Completeness Crisis

Field	Missing	Present	Completeness
Subject	182,430	47,487	20.6%
Author	78,269	151,648	66.0%
Title	51,514	178,403	77.6%
Created Date	3,260	226,657	98.6%

Title Quality Breakdown

Title Type	Count	Percentage
Missing (None)	51,514	22.4%
Generic "Document"	11,699	5.1%
"untitled"	2,081	0.9%
Meaningful titles	~165,000	71.6%

Top Authors

Author	Count
U.S. Government Printing Office	11,838
Unknown	3,477
Administrator	1,630
U.S. Government Accountability Office	1,390

Top Subjects

Subject	Count
Extracted Pages	11,692
NIOSH HHE REPORT	466
CMS Opinion Template	353
SEC Financial Proposals Summary	230

Processing Errors

Error Type	Count	Percentage
Could not read Boolean object	46	49.5%
cryptography>=3.1 required for AES	15	16.1%
Stream ended unexpectedly	9	9.7%
'NullObject' has no attribute 'get'	5	5.4%
Other errors	18	19.4%

Temporal Coverage

Date Field	Range	Issues
Modified Date	1979-12-31 to 2025-03-31	(dates in 2023-2025 are incorrect/defaulted to)
Created Date	Various formats	1,573 invalid "D:00000101000000Z"

Critical Assessment

Generated by Claude Sonnet-4, unsolicited (as always)

Data Quality Issues

Issue	Severity	Impact
Metadata Poverty	CRITICAL	79% missing subjects kills discoverability
Title Degradation	HIGH	28% generic/missing titles
Date Inconsistencies	MEDIUM	Invalid formats, future dates
Processing Errors	LOW	0.04% failure rate acceptable

Key Insights

Document Profile: Typical government PDF = 10 pages, 0.15 MB, metadata-poor

Fatal Flaw: This dataset has excellent technical extraction (99.96% success) but catastrophic intellectual organization. You're essentially working with 230K unlabeled documents.

Bottom Line: The structural data is solid, but without subject classification for 79% of documents, this is an unindexed digital landfill masquerading as an archive.