Dataset · Hugging Face

NEAR-AI/officeqa

Name: officeqa
Creator: NEAR-AI
License: cc-by-sa-4.0
Keywords: task_categories:question-answering, language:en, license:cc-by-sa-4.0, size_categories:n<1K, format:parquet, modality:text, library:datasets, library:pandas, library:polars, library:mlcroissant, region:us, agents, long-context, document-qa, evaluation

OfficeQA manifest (nearai-bench packaging) Harness-ready question manifest for databricks/officeqa — document-grounded QA over U.S. Treasury Bulletins (1939–2025). 246 items in full, 8 in smoke (a 4-easy/4-hard subset for pipeline checks). from datasets import load_dataset ds = load_dataset("NEAR-AI/officeqa", split="full") ⚠️ This is the manifest only — documents are NOT included Unlike our pinchbench and clawbench exports, the source corpus is not bundled here.… See the full description on the dataset page: https://huggingface.co/datasets/NEAR-AI/officeqa.

View on Hugging FaceReport an issue

Author / organization	NEAR-AI
License (as declared)	cc-by-sa-4.0
Downloads	18.5K (as reported by Hugging Face)
Likes	0
Created	24 July 2026
Last updated at source	24 July 2026

agentslong-contextdocument-qaevaluationofficeqa

Embed this entry

<iframe src="https://silytics.si/embed/item/086ca7de-89ab-4486-82f3-7713b449a2e6" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 14 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.