README.md

metadata

title: FinLongDocQA Viewer
emoji: 📈
colorFrom: green
colorTo: blue
sdk: static
pinned: false
license: other

📈 FinLongDocQA Viewer

Static viewer for FinLongDocQA — a benchmark for document-level numerical reasoning across single and multiple tables in long financial annual reports.

• Paper: https://arxiv.org/abs/2604.03664
• Code: https://github.com/AI-Application-and-Integration-Lab/FinLongDocQA
• QA dataset: https://huggingface.co/datasets/Amian/FinLongDocQA
• Report corpus (markdown, re-hosted): https://huggingface.co/datasets/timchen0618/finlongdocqa-reports

Two tabs:

• 📚 Corpus — the 1,456 annual reports (TICKER · YEAR). A searchable dropdown selects a report (filter by ticker, fiscal year, or whether it has questions). Each report is a long 10-K converted to Markdown; the panel renders it page-by-page (page navigator + jump-to-page) and streams the markdown on demand from the HF dataset CDN. Page numbers match the evidence pages cited in the Eval tab.
• ❓ Eval — the 7,527 numerical-reasoning questions. Each shows the question type (table / text / mixed), the ground-truth numeric answer, the chain-of-thought reasoning trace, the executable Python that computes the answer, and clickable evidence-page chips that jump straight to the cited page of the source report in the Corpus tab.

Data

File	Contents
dataset_qa.jsonl	7,527 QA examples (source, from Amian/FinLongDocQA)
eval.json	7,527 derived question records (Eval tab)
corpus_index.json	1,456 derived report records (Corpus tab)

The two derived files are regenerated with:

python scripts/build_data.py            # reads dataset_qa.jsonl + the reports/ tree

The ~1 GB markdown corpus is not bundled. The annual reports were downloaded from the project's Google Drive, sanitized to valid UTF-8, and re-hosted on the timchen0618/finlongdocqa-reports HF dataset. Each corpus_index.json record's md_url points at that dataset's CDN, which serves markdown inline with permissive CORS, so the viewer fetch()es and renders each report client-side (via marked) without the Space hosting the corpus.

Local dev

python -m http.server 8000   # then open http://localhost:8000/

Serves statically — no build step. Push to the HF Space remote to deploy.

Dataset license: AI²Lab Source Code License (National Taiwan University).