| --- |
| license: mit |
| language: |
| - en |
| task_categories: |
| - question-answering |
| - table-question-answering |
| tags: |
| - financial |
| - numerical-reasoning |
| - table-qa |
| - earnings-reports |
| size_categories: |
| - 1K<n<10K |
| dataset_info: |
| features: |
| - name: id |
| dtype: string |
| - name: filename |
| dtype: string |
| - name: pre_text |
| list: string |
| - name: post_text |
| list: string |
| - name: table |
| list: |
| list: string |
| - name: table_ori |
| list: |
| list: string |
| - name: question |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: exe_ans |
| dtype: string |
| - name: explanation |
| dtype: string |
| - name: ann_table_rows |
| list: int32 |
| - name: ann_text_rows |
| list: int32 |
| - name: steps |
| struct: |
| - name: op |
| list: string |
| - name: arg1 |
| list: string |
| - name: arg2 |
| list: string |
| - name: res |
| list: string |
| - name: program |
| dtype: string |
| - name: program_re |
| dtype: string |
| - name: gold_inds |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 32780740 |
| num_examples: 6251 |
| - name: validation |
| num_bytes: 4573763 |
| num_examples: 883 |
| - name: test |
| num_bytes: 5911480 |
| num_examples: 1147 |
| download_size: 39700298 |
| dataset_size: 43265983 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| - split: test |
| path: data/test-* |
| --- |
| |
| # FinQA |
|
|
| A full-fidelity repackaging of the [FinQA dataset](https://github.com/czyssrs/FinQA) (Chen et al., EMNLP 2021) for numerical reasoning over financial tables. |
|
|
| FinQA contains questions over earnings reports from S&P 500 companies (1999–2019), sourced from the [FinTabNet dataset](https://developer.ibm.com/exchanges/data/all/fintabnet/). Each example pairs a financial table and surrounding text with a question, a human-readable answer, and a structured reasoning program that specifies the arithmetic operations needed to derive the answer. |
|
|
| ## Why this version |
|
|
| The existing HuggingFace copy ([dreamerdeo/finqa](https://huggingface.co/datasets/dreamerdeo/finqa)) omits `program`, `program_re`, and `steps` — the reasoning annotations that are FinQA's primary contribution. This version preserves the complete schema from the canonical GitHub release, excluding only model-artifact fields (`table_retrieved*`, `text_retrieved*`, `qa.tfidftopn`, `qa.model_input`) that were bundled with the original retriever checkpoint. |
|
|
| ## Splits |
|
|
| | Split | Examples | |
| |---|---| |
| | train | 6,251 | |
| | validation | 883 | |
| | test | 1,147 | |
|
|
| The private leaderboard test set (no ground-truth references) is intentionally excluded. |
|
|
| ## Schema |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | string | Unique example ID: `{TICKER}/{YEAR}/page_{N}.pdf-{idx}` | |
| | `filename` | string | Source PDF path: `{TICKER}/{YEAR}/page_{N}.pdf` | |
| | `pre_text` | list[string] | Text passages before the table | |
| | `post_text` | list[string] | Text passages after the table | |
| | `table` | list[list[string]] | Normalized table (numbers unformatted, HTML stripped) | |
| | `table_ori` | list[list[string]] | Original table with HTML formatting (superscripts, comma-separated numbers) | |
| | `question` | string | The financial question | |
| | `answer` | string | Human-readable answer (e.g. `"93.5%"`) | |
| | `exe_ans` | string | Raw execution result as string; numeric (e.g. `"0.935"`) or `"yes"`/`"no"` | |
| | `explanation` | string | Free-text explanation; sparse (~16% non-empty) | |
| | `ann_table_rows` | list[int] | Zero-indexed table rows annotated as gold evidence | |
| | `ann_text_rows` | list[int] | Zero-indexed text passages annotated as gold evidence | |
| | `steps` | struct | Structured execution steps (see below) | |
| | `program` | string | Flat DSL program with `#N` back-references (e.g. `subtract(920, 95), divide(#0, 5)`) | |
| | `program_re` | string | Fully nested program form (e.g. `divide(subtract(920, 95), 5)`) | |
| | `gold_inds` | string | JSON-serialized dict mapping evidence keys to text (e.g. `{"table_3": "..."}`) | |
|
|
| ### `steps` struct |
|
|
| Each element of `steps` represents one arithmetic operation: |
|
|
| | Subfield | Type | Description | |
| |---|---|---| |
| | `op` | string | Operation name with positional suffix (e.g. `minus2-1`, `divide1-2`) | |
| | `arg1` | string | First argument: a literal value or `#N` reference | |
| | `arg2` | string | Second argument: a literal value, `const_*`, or `#N` reference | |
| | `res` | string | Result of this step (intermediate or final) | |
|
|
| Programs range from 1 to 5 steps. `const_100`, `const_1000`, etc. are predefined constants. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("rootsautomation/FinQA") |
| ex = ds["train"][0] |
| |
| print(ex["question"]) |
| print(ex["answer"]) # human-readable |
| print(ex["exe_ans"]) # raw numeric or yes/no |
| print(ex["program"]) # flat DSL |
| print(ex["program_re"]) # nested DSL |
| print(ex["steps"]) # structured ops |
| |
| import json |
| gold = json.loads(ex["gold_inds"]) # {key: evidence_text} |
| ``` |
|
|
| ## Image collation |
|
|
| This dataset is text-only. The source PDF page images are available in FinTabNet (CDLA-Permissive-1.0). The `id` field (`{TICKER}/{YEAR}/page_{N}.pdf-{idx}`) and `filename` field (`{TICKER}/{YEAR}/page_{N}.pdf`) provide a direct join key into FinTabNet's file structure, making it possible to construct an image-grounded version of FinQA. |
|
|
| ## License |
|
|
| QA annotations: [MIT](https://github.com/czyssrs/FinQA/blob/main/LICENSE). |
|
|
| Underlying table data is sourced from FinTabNet, which is released under [CDLA-Permissive-1.0](https://cdla.dev/permissive-1-0/). The original earnings reports are publicly available SEC filings. The FinQA paper (§2) explicitly verified that CDLA-Permissive-1.0 permits creating and publishing additional annotations over FinTabNet data. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{chen-etal-2021-finqa, |
| title = "{F}in{QA}: A Dataset of Numerical Reasoning over Financial Data", |
| author = "Chen, Zhiyu and Chen, Wenhu and Smiley, Charese and Shah, Sameena and Borova, Iana and Langdon, Dylan and Moussa, Reema and Beane, Matt and Huang, Ting-Hao and Routledge, Bryan and Wang, William Yang", |
| booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", |
| year = "2021", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2021.emnlp-main.300", |
| } |
| ``` |
|
|
