aditya487/cbi-archive-corpus

Central Bank of Ireland Public Archive Corpus

A page-anchored, provenance-classified corpus of the Central Bank of Ireland's public document archive. 5,568 documents, 88,783 source pages and 190,941,651 characters, in about 48 MB of Parquet (about 51 MB including compressed audit manifests).

This is an unofficial derived work. It is not published by, affiliated with, or endorsed by the Central Bank of Ireland.

What makes this different from a pile of scraped PDFs

Two things.

Every page keeps its address. Each row carries the source document's SHA-256, its URL, and its page number in the original PDF. A claim found here can be traced back to a specific page of a specific file whose bytes you can verify. Nothing is a floating snippet.

Every page is classified by who is speaking. The Central Bank hosts two very different kinds of document: material it authored, and submissions that banks, insurers, trade bodies and individuals sent it during public consultations. Those read alike and mean the opposite. A submission from a bank arguing that a rule is burdensome is not a regulatory finding, and treating it as one is the single easiest way to produce confidently wrong analysis from this archive.

The document-level authorship column separates ordinary documents, while the page table can separate voices inside a composite:

authorship	documents	what it means
central-bank	3,844	the regulator speaking
stakeholder	1,722	a respondent writing to the regulator: advocacy or consultation input
mixed	2	a composite; use page-level authorship
unresolved	0	retained as a valid schema value, but no v5.1 document remains unresolved

unresolved remains a real answer, not a fallback to central-bank. In v5.1, all 89 formerly unresolved records were adjudicated from their opening and closing pages. The SHA-keyed decisions are published rather than hidden in classifier code: 37 are Central Bank, 51 stakeholder and one mixed.

Why the classifier is not a keyword match

An earlier version of this classification treated a consultation-hosted document as a submission only when its filename contained the string response. That missed three whole families:

1. files named cp45-submission-from-aib.pdf
2. files named cp51-feedback-from-generali-paneurope.pdf, which were being filed as Central Bank feedback statements
3. files published under the responder's bare name, such as blackrock.pdf, which carry no cue in the filename at all

Family 3 cannot be resolved from a filename, so classification is two-pass: filename attribution and Central Bank document-type cues first, then the opening pages of the document itself where the filename is silent. Only a narrow set of issuer-specific Central Bank cues takes precedence over stakeholder attribution, which is what keeps note-from-the-financial-regulator-in-relation-to-submissions-received-on-cp43 correctly attributed to the Bank. Generic document-type cues do not: they apply only where the filename carries no attribution, because letting them win is the defect that put AIB's and Bank of Ireland's submissions into the regulator's pile in an earlier version. The rule that produced each label is stored in classification_basis, with a classification_confidence of high, medium or low. 116 regression assertions cover known failure modes. A separate reviewed 32-document regression set includes one mixed composite and two audited submissions outside consultation folders. It was used during development, so it is an error detector, not an independent holdout or a population accuracy estimate.

Files

File	Rows	Size	Contents
data/documents.parquet	5,568	1.19 MB	one row per document: URL, hashes, publication evidence, title, authorship, engagement, exact-content cluster and extraction metadata
data/pages.parquet	88,783	46.8 MB	one row per source page, carrying full text and page-level authorship
manifests/files.csv.zst	6,984	0.6 MB	the original download manifest: every URL, its SHA-256, bytes, content type, referrers
manifests/conversion-manifest.csv.zst	5,246	0.8 MB	PDF to Markdown conversion record, per document
manifests/conversion-manifest-office.csv.zst	323	51 KB	the same for Word, Excel, PowerPoint and ZIP sources
manifests/provenance-classification.csv.zst	5,568	0.3 MB	full classification audit trail, including the previous label
manifests/extraction-quality.csv.zst	5,568	0.3 MB	per-document extraction fidelity grades
manifests/authorship-overrides.csv.zst	89	6 KB	SHA-keyed adjudications of every formerly unresolved document
manifests/page-authorship-overrides.csv.zst	4	<1 KB	audited page ranges for the two mixed composites
manifests/conversion-exclusions.csv.zst	1	<1 KB	the one downloaded HTML error body excluded from conversion
manifests/engagement-coverage.csv.zst	182	2 KB	canonical CP/DP identifiers and document-class coverage
manifests/manifest-build-summary.json	1	<1 KB	hashes and row counts for every compressed manifest

The split is deliberate. Most questions need only documents.parquet, which is under a megabyte, and never touch the page text at all.

Usage

from datasets import load_dataset

docs = load_dataset("aditya487/cbi-archive-corpus", "documents", split="train")
pages = load_dataset("aditya487/cbi-archive-corpus", "pages", split="train")

Or query it without downloading anything, using DuckDB over HTTPS:

INSTALL httpfs; LOAD httpfs;
SELECT d.title, p.page_number, substr(p.text, 1, 200)
FROM  'https://huggingface.co/datasets/aditya487/cbi-archive-corpus/resolve/main/data/pages.parquet' p
JOIN  'https://huggingface.co/datasets/aditya487/cbi-archive-corpus/resolve/main/data/documents.parquet' d
  USING (document_id)
WHERE p.authorship = 'central-bank'
  AND lower(p.text) LIKE '%operational resilience%'
LIMIT 20;

Parquet is columnar, so that query reads only the columns it names.

How the corpus was built

• Crawl snapshot: 25 August 2026, from centralbank.ie, www.centralbank.ie and the open-data portal, using the sitemap plus the CKAN open-data API.
• Downloaded: 6,963 files, 7.476 GB, with 21 URLs failing (all HTTP 404 and all recorded rather than silently dropped).
• Deduplicated by SHA-256: 6,309 unique files. 654 files were byte-identical content served at more than one URL.
• Converted: PDFs through PyMuPDF4LLM with page anchors preserved, selective RapidOCR for scanned documents, and a separate pipeline for the 323 DOCX, DOC, ZIP and PPTX files that a PDF-only filter had originally skipped.
• Validated: every source hash and every output hash recomputed from disk, page markers checked for sequence, orphans checked for. Zero failures.
• Engagement identifiers normalised: cp071, cp-71 and CP71 map to cp71; discussion papers similarly map to dpN. The snapshot contains 149 CP identifiers, 11 DP identifiers and 73 CPs with a proposal, stakeholder responses and a Central Bank feedback statement. The 22 gaps in CP1 to CP171 are published as snapshot coverage gaps, not asserted to be unissued numbers.
• Exact-content clusters retained: 5,490 exact-text clusters cover 5,568 documents; 146 documents belong to a cluster larger than one. Seven are the empty-document cluster, leaving 139 duplicated non-empty documents and 72 excess non-empty records if text is counted once. They remain as separate records because their URLs and publication contexts differ. Use content_cluster_id to avoid double-counting identical text in analysis.

Limitations, stated plainly

• page_basis is not always a page. PDFs carry true source-page anchors. Office documents mostly have no page structure: 179 of the 323 are a single pseudo-page, meaning the anchor identifies the document, not a location within it. Only source-page and slide are safe to cite as positions.
• 87 documents are graded below ok for extraction fidelity: 36 with substantially blank pages, 25 garbled by encoding damage, 19 thin, 7 empty. They are listed in extraction-quality.csv.zst. Chart-heavy statistical releases extract with visible damage even when their numbers survive intact.
• 441,610 characters were recovered in a later pass and are included here. The original converter returned nothing for pages carrying a full-page background image, even where a readable text layer sat underneath, so 1,167 pages were re-extracted directly and a further 258 image-only pages were recovered by OCR. A later targeted OCR pass replaced five garbled CP76 pages with 11,206 readable characters. recovered-pages.csv in the GitHub repository is the cumulative OCR-page ledger; direct text-layer recoveries are reported separately in the extraction review.
• Publication dates are sparse by design. published_at is populated only where source/referrer metadata states an explicit date. analysis_year can additionally use a plausible PDF creation year, but future timestamps are rejected; raw pdf_creation_date is preserved as untrusted metadata.
• ZIP archives are profiled, not transcribed. Most are XBRL taxonomy packages; one held 31,185 schema files. Transcribing them produced 705 MB of Markdown and would have made machine schema dominate every word count. The member listing and namespace profile are included; schema bodies are not.
• Publication volume is not importance. A topic appearing in many documents usually means a consultation drew many responses, not that the topic matters more. Consultation submissions are advocacy and are written to persuade.
• This is a dated snapshot. It cannot contain anything unpublished, unlinked, blocked, or published after 25 August 2026.

Rights, reuse and attribution

This is a mixed-rights corpus, so the machine-readable licence is deliberately other, not blanket CC BY 4.0. Only 71 open-data resources carry explicit CC BY 4.0 metadata. Most Central Bank-authored material is covered by the site's PSI reuse terms, while stakeholder submissions, personal information and other third-party material may fall outside that permission.

Contains Irish Public Sector Information licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence.

Use the attribution above when the PSI licence applies. The CKAN licence title and URL for each open-data resource are retained in manifests/files.csv.zst so exceptions can be checked. The licence excludes personal information, third-party rights, trademarks, crests, logos and official symbols, and prohibits any implication of endorsement.

See RIGHTS-REVIEW.md in the GitHub repository before redistributing text or using the corpus commercially. This metadata change is conservative and is not legal advice.

Modifications made here: conversion to Markdown and Parquet, deduplication by content hash, provenance classification, extraction-quality grading, and OCR replacement of five pages whose earlier text was 94% decoding noise. The original source bytes remain unchanged in the raw archive.

All generated CSV audit artifacts are UTF-8 without a byte-order mark and use LF line endings, so their locked hashes are stable across Windows and Unix.