Datasetpublic

FormulaComletion/fxbench

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… See the full description on the dataset page: https://huggingface.co/datasets/FormulaComletion/fxbench.

sourceHugging Faceotherupdated 5mo agoView on Hugging Face

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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"])