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File size: 6,793 Bytes
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"name": "FxBench",
"url": "https://huggingface.co/datasets/FormulaComletion/fxbench",
"license": "https://spdx.org/licenses/CC-BY-SA-4.0.html",
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"citeAs": "FxBench: A benchmark for inline spreadsheet formula completion.",
"datePublished": "2026-05-07",
"version": "1.0.0",
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"rai:dataLimitations": "fxbench is a manually curated benchmark of 503 spreadsheet formula suggestions. It is an evaluation-only benchmark not intended for model training or fine-tuning. It is specifically designed for evaluating large language models in inline formula suggestion settings. The benchmark curation filters out non-English spreadsheets, so not ideal for evaluating models on non-English data.",
"rai:dataBiases": "fxbench focuses on real-world spreadsheet examples, so we filter-out very small or very huge spreadsheets. We also filter-out spreadsheets with non-English content. The benchmark contains spreadsheets from a variety of domains, but we do not annotate domain metadata, so some domains may be non-uniformly represented.",
"rai:personalSensitiveInformation": "fxbench is derived from Sheetpedia, a corpus of 300k real-world spreadsheets. Sheetpedia was filtered to remove personally identifiable information (PII) and sensitive data, but we have not independently audited the dataset for residual PII or sensitive data. We also do not annotate records with potential residual PII or sensitive data, so some records may contain unmarked PII or sensitive data.",
"rai:dataUseCases": "fxbench is only intended as evaluation benchmark for inline spreadsheet formula suggestion systems.",
"rai:dataSocialImpact": "fxbench is a benchmark for spreadsheet formula suggestions, which has no direct social impact beyond improving productivity tools.",
"rai:hasSyntheticData": false,
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"description": "A 300K spreadsheet corpus for spreadsheet intelligence and LLM fine-tuning",
"license": "https://spdx.org/licenses/CC-BY-SA-4.0.html"
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"http://www.w3.org/ns/prov#wasGeneratedBy": "fxbench is curated by first programmatically filtering, annotating, ranking and de-duplicating the source dataset, and then manually reviewing and curating the final benchmark set. Some parts of the process use LLM annotations, but every record in the final benchmark set is manually reviewed."
}
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