xfund-docai-xl

An adaptation of XFUND for key information extraction and document classification DocAI tasks with augmented generated documents.

Note: Annotations and Synthetic documents have been generated by Claude Opus 5 under my supervision - but errors and biases may be here and there. The dataset is intended for experimenting with LLMs finetuning for DocAI. If you spot something odd and would like to contribute to improve the quality, feel free to open an issue!

Seven languages, two tasks, one prompt shape. 1 743 documents turned into 3 027 chat examples that teach a small model to (a) fill whatever JSON schema it is handed and (b) say what kind of document it is looking at — in Italian, German, Spanish, French, Portuguese, Chinese and Japanese.

Derived from XFUND plus 350 fabricated pages. Successor to xfund-kie-it (Italian, extraction only).

	train	val
documents	1 323	420
extraction rows	1 221	385
classification rows	1 083	338

1 393 XFUND pages (149 train + 50 val × 7 languages, its own split) and 350 generated pages (40 + 10 × 7). 9 105 extracted fields — 6 686 from XFUND, 2 419 synthetic — over 277 keys and 12 classes. A page contributes a row to each task it can answer, so most appear twice and 57 answer neither.

What is new here

• Seven languages, not one. The Italian label→key pipeline was rewritten as a per-language rule table; every language has its own key vocabulary.
• One language per document, throughout. Keys, their descriptions and the class names are all in the document's own language. xfund-kie-it named Italian keys and then explained them in English, which asks the model to switch language mid-prompt.
• A second task. Document classification, sharing the system-prompt shape with extraction so one fine-tune teaches both.
• A cross-lingual index. concepts.json maps each language's key onto a shared concept (luogo-nascita, geburtsort, 出生地 → birthplace), so the same field can be scored across languages. 78 concepts, 23 of them present in five languages or more. The concept ids never reach the model.
• Generated pages. 350 documents covering all 12 classes across 6 page layouts and 4 visual themes, shipped as HTML alongside the text.

Prompt shape

Both tasks open the same way, then diverge on one line.

You are an expert document analysis model.
Task: information extraction
Return a JSON object with exactly the keys listed below, in the same order. Every
value must be copied verbatim from the document. Omit a key whose value is absent.

Schema:
cognome: il cognome della persona.
nome: il nome di battesimo della persona.
data-nascita: la data di nascita della persona.
codice-fiscale: il codice fiscale di una persona o di un ente.
data-rilascio: la data di rilascio di un documento.

→ {"cognome": "VALLE", "nome": "LUISA", "data-nascita": "22/12/1977", "codice-fiscale": "VAL6647338477433", "data-rilascio": "2/12/2019"} (it_val_0)

You are an expert document analysis model.
Task: document classification
Assign the document to exactly one of the classes listed below. Answer with a JSON
object of the form {"class": "<class>"}.

Classes:
領収書: 代金が支払われたこと、または物品を受領したことを示す書面.
委任状: 本人が代理人に手続きを委任することを示す書面.
… (all twelve, shuffled)

→ {"class": "委任状"}

Files

train.jsonl / val.jsonl	the dataset: document_id, lang, task, source, document_class, messages
schemas.json	per language, key → description (both in that language)
classes.json	the 12 class ids, each with a name and gloss per language
stats.json	counts per language, split and class

Synthetic documents carry document_id synthetic_0 … synthetic_349; XFUND pages keep theirs (it_val_0). Images are not copied — documents/*.json points at ../xfund/.

How it was built

Extraction. XFUND ships grounded question→answer links: a label on the page points at the value written next to it. rules.py maps the label surface form onto a key (Codice Fis.:, cod. fiscale, C.F. → codice-fiscale), so every value is verbatim page text and nothing is generated. Then, in order: a label linked to several answers is a repeated field and cannot supply one value; a key asserted twice with different values is ambiguous and leaves the page; a value that cannot have the field's shape (an email with no @, an age that is a town) is a mis-drawn link and is dropped; a key seen fewer than four times in train is neither learnable nor scoreable and leaves the schema. 26–46% of links map to a key — the rest are instruction paragraphs, checkbox prompts and table headers.

Classification. XFUND has no class labels, so they are read off each page's own title (Vollmacht → power_of_attorney, 登録用紙 → registration_form). 1 071 of 1 393 XFUND pages state what they are; the other 322 are left out of the classification task rather than guessed at. They stay in the extraction task.

Generated pages. One document is built once as a list of blocks, then rendered twice — to the text the model reads and to an HTML page — so the two cannot drift. Everything in them is invented: people, towns, companies, tax codes (right shape, wrong check character), IBANs; email addresses are on example.<tld>. Rare classes are over-weighted, since application and registration forms are 72% of what XFUND labels (57% once the generated pages are added).

Rebuild and check, from local/:

python make_docai.py      # writes xfund-docai-xl/
python verify_docai.py    # re-reads it and checks it

Decisions worth knowing

• The schema lists exactly the keys the page answers, in the order it answers them. The lesson is mirror the schema you are given; at inference the caller supplies it. No distractor keys — so a score on this val split is an upper bound on a run where the caller does not already know which fields are present.
• {"class": "<name>"}, not a dict of all classes with one true. Shorter, and it keeps both tasks answering with one JSON object. The full class list is always shown and shuffled per example, so position cannot predict the answer.
• telefono and cellulare are separate keys (and their equivalents elsewhere). xfund-kie-it merged them, which made every page carrying both ambiguous and dropped it.
• Class ids are English, class names are not. The id is what stats.json and document_class record; the model only ever sees the localized name.

Limitations

• Japanese is the thinnest — 27 keys, 3.1–3.8 fields per page. Japanese forms repeat 住所 and 氏名 for applicant, guarantor and agent, and the ambiguity rule correctly drops all of them.
• Class labels are heuristic, read from titles, not human-annotated. A title that lies about its document, or a class-word that appears in a subtitle, is mislabelled.
• Generated pages have no OCR noise; XFUND pages have plenty. A model trained on the mix should still be evaluated on scanned input.
• XFUND's links are hand-drawn and occasionally land on the wrong box. The shape guards catch what they can (a phone number that is a street), not what they cannot (a first name in the surname box).

Licence and attribution

Upstream: XFUND, https://github.com/doc-analysis/XFUND, CC BY-NC-SA 4.0. This is a modified derivative, so the same licence applies: CC BY-NC-SA 4.0, NonCommercial, ShareAlike. Keep the attribution if you publish this dataset or a model trained on it. The 350 generated pages are original to this dataset and released under the same terms for simplicity.