williamTLmiller/matrixnorm-docevidence-v1-sample

MatrixNorm DocEvidence v1 (free sample)

Grounded document QA with exact citations over multi-page PDFs: invoices with long line tables, policies with effective-dated revisions, equipment manuals, quarterly reports and service agreements. Every answer cites the exact character span on the exact page that supports it, every page region has a bounding box, and tables are reconstructed as logical tables even when they continue across pages (the header row repeats on each page). About one question in five is deliberately unanswerable from the documents and is paired with an explicit abstention, and the same question wording appears answerable in other families, so abstention has to come from reading, not from the question template.

This is the free public sample: whole train groups only (about 5% of them). Validation and test rows stay in the private full release so they remain unseen. Splits below are therefore train only.

Rows

View	train
document_qa	1,811
document_structure	358

from datasets import load_dataset
ds = load_dataset("williamTLmiller/matrixnorm-docevidence-v1-sample", "document_qa")

Each view is also stored as JSON Lines next to the Parquet file, with identical rows. Nested fields (messages, citations, source_refs, tables) are native lists and structs in both.

Views. document_qa: question, the documents it is about (document_id, PDF uri, revision), answer, citations (document_id, page_index, selector = codepoints:start:end into that page's text, and the exact quoted_text) and answerability. document_structure: per document, the pages, regions (type, bbox in PDF points with a top-left origin, text), logical tables with cells, and table continuations across pages. Join QA to structure with document_qa.documents[].document_id == the structure row's sample_id minus the de-doc- prefix. The PDFs are in pdfs/.

A citation's selector indexes the page text, which you can rebuild exactly from the structure view: a page has 49 lines (48 body lines, then the footer), line i starts at y = 54 + 14 * i points, so each region's text (which may span several lines) starts at line (bbox[1] - 54) / 14; fill every other line with an empty string and join the 49 lines with \n.

How it was built

Documents are generated from seeded templates, laid out deterministically (US Letter, Courier 10 pt, 6 pt per character and 14 pt per line, so every box is exact) and written as PDFs. Each PDF is checked independently with poppler pdftotext -bbox: the page count must match, and every word must fall inside a declared region. Each citation is re-located in the page text, its slice must equal the quoted value, and numeric answers must parse. Policy families add a second, later-effective revision, version-sensitive questions and a conflict that is resolved by effective date. Groups = one document family.

Validation

Every record is checked before it is written: schema, cross-references, split membership, and the dataset-specific checks described above. A family that fails any check is quarantined as a whole, never silently dropped or partially kept.

Build stats: {"documents": 7200, "families": 6000, "kinds": {"conflict_resolution": 1200, "cross_page": 438, "extractive": 14383, "numeric": 7207, "table_lookup": 4800, "unanswerable": 6010, "version_sensitive": 2400}, "pages": 7929, "questions": 36438}

Trainability baseline

Measured on the full release's held-out splits (this sample is train only). Task: predict answerability (answerable vs unanswerable) from the question and the documents' text. gradient-boosted trees blended with extra trees (CPU) on tf-idf -> truncated SVD text features plus simple structural counts, trained on 35,712 train rows, no GPU:

Split	Rows	Accuracy	Macro-F1	Majority class
validation	363	0.995	0.991	0.813
test	363	0.997	0.995	0.826

Ablation: the same model given only the question text (no document features) scores 0.846 accuracy and 0.700 macro-F1 on test, against a 0.826 majority class. Answerability comes from reading the documents, not from the wording of the question.

Limitations

• Documents are synthetic and typeset in a monospaced font, with no scans, handwriting, images or multi-column layouts. Use it to teach citation discipline, table continuation and abstention, and pair it with real scanned data for visual robustness.
• Questions come from templates, so wording varies less than real user queries.

Licensing

CC BY-NC 4.0 (see LICENSE and NOTICE). The whole dataset is MatrixNorm-generated content. Commercial licensing: energea@energeatech.com.

Citation

MatrixNorm DocEvidence v1. williamTLmiller, MatrixNorm, 2026. https://huggingface.co/datasets/williamTLmiller/matrixnorm-docevidence-v1-sample