| --- |
| license: other |
| pretty_name: FxBench |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-generation |
| tags: |
| - spreadsheets |
| - excel |
| - formula-completion |
| --- |
| |
| # FxBench |
|
|
| FxBench is a spreadsheet formula-completion benchmark. Each row contains |
| one target formula and the corresponding workbook snapshot needed to solve it. |
|
|
| ## Dataset contents |
|
|
| This release contains 503 examples. |
|
|
| Columns in the Hugging Face Parquet split: |
|
|
| | Column | Type | Description | |
| | --- | --- | --- | |
| | `id` | string | Public example ID in the form `fxbench-{i}`. | |
| | `function` | string | Primary Excel function for the target formula. | |
| | `formula` | string | Ground-truth Excel formula. Empty for `ABSTAIN` rows where no formula response is expected. | |
| | `target_cell` | string | Cell address where the formula belongs. | |
| | `target_sheet` | string | Worksheet containing the target cell. | |
| | `workbook` | binary | XLSX workbook snapshot bytes. | |
|
|
| The auxiliary `metadata.jsonl` manifest contains scalar public metadata only; workbook bytes are in the Parquet split. |
| The loadable split is `data/train-00000-of-00001.parquet` and stores the `workbook` column as XLSX bytes. |
|
|
| Workbook bytes are sanitized before export to remove document properties, |
| classification-label metadata, custom properties, comment/person metadata, and |
| local path markers while preserving workbook sheets and links. |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("<your-namespace>/<your-dataset-repo>", split="train") |
| row = ds[0] |
| |
| with open(f"{row['id']}.xlsx", "wb") as f: |
| f.write(row["workbook"]) |
| ``` |
|
|
