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Add FinanceBench viewer: Corpus + Eval tabs
Browse filesStatic viewer for the open-source FinanceBench dataset.
- Corpus tab: 368 corpus PDFs, searchable dropdown by doc title, inline PDF render + metadata.
- Eval tab: 150 annotated questions with gold answer, justification, reasoning, evidence spans, and doc link.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This view is limited to 50 files because it contains too many changes. See raw diff
- .gitattributes +2 -35
- README.md +47 -5
- corpus_index.json +0 -0
- financebench_document_information.jsonl +361 -0
- financebench_open_source.jsonl +0 -0
- index.html +96 -18
- pdfs/3M_2015_10K.pdf +3 -0
- pdfs/3M_2016_10K.pdf +3 -0
- pdfs/3M_2017_10K.pdf +3 -0
- pdfs/3M_2018_10K.pdf +3 -0
- pdfs/3M_2019_10K.pdf +3 -0
- pdfs/3M_2020_10K.pdf +3 -0
- pdfs/3M_2021_10K.pdf +3 -0
- pdfs/3M_2022_10K.pdf +3 -0
- pdfs/3M_2023Q2_10Q.pdf +3 -0
- pdfs/ACTIVISIONBLIZZARD_2015_10K.pdf +3 -0
- pdfs/ACTIVISIONBLIZZARD_2016_10K.pdf +3 -0
- pdfs/ACTIVISIONBLIZZARD_2017_10K.pdf +3 -0
- pdfs/ACTIVISIONBLIZZARD_2018_10K.pdf +3 -0
- pdfs/ACTIVISIONBLIZZARD_2019_10K.pdf +3 -0
- pdfs/ACTIVISIONBLIZZARD_2020_10K.pdf +3 -0
- pdfs/ACTIVISIONBLIZZARD_2021_10K.pdf +3 -0
- pdfs/ACTIVISIONBLIZZARD_2022_10K.pdf +3 -0
- pdfs/ACTIVSIONBLIZZARD_2023Q2_10Q.pdf +3 -0
- pdfs/ADOBE_2015_10K.pdf +3 -0
- pdfs/ADOBE_2016_10K.pdf +3 -0
- pdfs/ADOBE_2017_10K.pdf +3 -0
- pdfs/ADOBE_2018_10K.pdf +3 -0
- pdfs/ADOBE_2019_10K.pdf +3 -0
- pdfs/ADOBE_2020_10K.pdf +3 -0
- pdfs/ADOBE_2021_10K.pdf +3 -0
- pdfs/ADOBE_2022Q2_10Q.pdf +3 -0
- pdfs/ADOBE_2022_10K.pdf +3 -0
- pdfs/AES_2015_10K.pdf +3 -0
- pdfs/AES_2016_10K.pdf +3 -0
- pdfs/AES_2017_10K.pdf +3 -0
- pdfs/AES_2018_10K.pdf +3 -0
- pdfs/AES_2019_10K.pdf +3 -0
- pdfs/AES_2020_10K.pdf +3 -0
- pdfs/AES_2021_10K.pdf +3 -0
- pdfs/AES_2022_10K.pdf +3 -0
- pdfs/AMAZON_2015_10K.pdf +3 -0
- pdfs/AMAZON_2016_10K.pdf +3 -0
- pdfs/AMAZON_2017_10K.pdf +3 -0
- pdfs/AMAZON_2018_10K.pdf +3 -0
- pdfs/AMAZON_2019_10K.pdf +3 -0
- pdfs/AMAZON_2020_10K.pdf +3 -0
- pdfs/AMAZON_2021_10K.pdf +3 -0
- pdfs/AMAZON_2022_10K.pdf +3 -0
- pdfs/AMCOR_2019_10K.pdf +3 -0
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README.md
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---
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title:
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emoji:
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colorFrom:
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sdk: static
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pinned: false
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---
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-
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---
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title: FinanceBench Viewer
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emoji: 🏦
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colorFrom: green
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colorTo: gray
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sdk: static
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pinned: false
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license: cc-by-nc-4.0
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---
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# FinanceBench Viewer
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Static viewer for the open-source [FinanceBench](https://github.com/patronus-ai/financebench)
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financial-QA dataset ([HF dataset](https://huggingface.co/datasets/PatronusAI/financebench)).
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Two tabs:
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- **📚 Corpus** — every document in the corpus (368 SEC filings / earnings PDFs).
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A searchable dropdown selects a document by title (`doc_name`); the panel shows
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its metadata (company, GICS sector, doc type, period, source link) and renders
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the PDF inline. Filter the dropdown by name/company, GICS sector, or doc type.
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- **❓ Eval** — the 150 annotated questions. Each shows the question, gold answer,
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justification, reasoning type, question type, company, the source `doc_name`
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(click to jump to it in the Corpus tab), and every evidence span (page number,
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extracted evidence text, and the full-page extract).
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## Data
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| File | Contents |
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|---|---|
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| `financebench_open_source.jsonl` | 150 annotated questions (Eval tab) |
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| `financebench_document_information.jsonl` | 360 per-document metadata rows |
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| `corpus_index.json` | 368 corpus docs = PDF listing ⋈ metadata (built by `scripts/build_data.py`) |
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| `pdfs/*.pdf` | 368 source PDFs, ~674 MB, git-LFS tracked |
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`corpus_index.json` is regenerated from the two source files + the `pdfs/`
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listing with:
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```bash
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python scripts/build_data.py
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```
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## Local dev
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```bash
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python -m http.server 8000 # then open http://localhost:8000/
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```
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Serves statically — no build step. Push to the HF Space remote to deploy.
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Source: patronus-ai/financebench (150-example open-source sample; the full
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FinanceBench has 10,231 questions). Dataset license: CC-BY-NC-4.0.
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corpus_index.json
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The diff for this file is too large to render.
See raw diff
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financebench_document_information.jsonl
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{"doc_name":"3M_2015_10K","company":"3M","gics_sector":"Industrials","doc_type":"10k","doc_period":2015,"doc_link":"https:\/\/investors.3m.com\/financials\/sec-filings\/content\/0001558370-16-003162\/0001558370-16-003162.pdf"}
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{"doc_name":"3M_2016_10K","company":"3M","gics_sector":"Industrials","doc_type":"10k","doc_period":2016,"doc_link":"https:\/\/investors.3m.com\/financials\/sec-filings\/content\/0001558370-17-000479\/0001558370-17-000479.pdf"}
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{"doc_name":"3M_2017_10K","company":"3M","gics_sector":"Industrials","doc_type":"10k","doc_period":2017,"doc_link":"https:\/\/investors.3m.com\/financials\/sec-filings\/content\/0001558370-18-000535\/0001558370-18-000535.pdf"}
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{"doc_name":"3M_2018_10K","company":"3M","gics_sector":"Industrials","doc_type":"10k","doc_period":2018,"doc_link":"https:\/\/investors.3m.com\/financials\/sec-filings\/content\/0001558370-19-000470\/0001558370-19-000470.pdf"}
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{"doc_name":"3M_2019_10K","company":"3M","gics_sector":"Industrials","doc_type":"10k","doc_period":2019,"doc_link":"https:\/\/investors.3m.com\/financials\/sec-filings\/content\/0001558370-20-000581\/0001558370-20-000581.pdf"}
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{"doc_name":"3M_2020_10K","company":"3M","gics_sector":"Industrials","doc_type":"10k","doc_period":2020,"doc_link":"https:\/\/investors.3m.com\/financials\/sec-filings\/content\/0001558370-21-000737\/0001558370-21-000737.pdf"}
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{"doc_name":"WALMART_2023_annualreport","company":"Walmart","gics_sector":"Consumer Staples","doc_type":"10k_annualreport","doc_period":2023,"doc_link":"https:\/\/s201.q4cdn.com\/262069030\/files\/doc_financials\/2023\/ar\/Walmart-10K-Reports-Optimized.pdf"}
|
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{"doc_name":"WALMART_2024Q1_10Q","company":"Walmart","gics_sector":"Consumer Staples","doc_type":"10q","doc_period":2024,"doc_link":"https:\/\/d18rn0p25nwr6d.cloudfront.net\/CIK-0000104169\/ad87f87c-b8ea-4aab-90d4-3a88b4662e12.pdf"}
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