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HiTab-StatCan-NSF
A commercially-permissive subset of Microsoft HiTab (ACL 2022) containing only the Statistics Canada and NSF table sources. The Wikipedia/ToTTo subset (CC-BY-SA-4.0, share-alike) is excluded.
License
- NSF tables (~14% of rows): sourced from US National Science Foundation reports. US federal government works are public domain under 17 U.S.C. § 105.
- StatCan tables (~86% of rows): sourced from Statistics Canada reports under the Statistics Canada Open Government Licence, which permits commercial use with attribution. Cite as: "Adapted from Statistics Canada. Reproduced and distributed on an 'as is' basis with the permission of Statistics Canada."
The original HiTab dataset is released under C-UDA-1.0. That agreement's Section 1.3 preserves your rights under any other applicable licence, so the per-source terms above govern the StatCan and NSF subsets.
Source
Built from microsoft/HiTab by
filtering table_source to statcan and nsf.
Content
Hierarchical table question answering and table-to-text generation covering
Canadian statistical reports (demographics, immigration, economics, health)
and US NSF science and engineering statistics. Tables have multi-level row
and column headers encoded as HTML with rowspan/colspan and as raw JSON.
Splits
| Split | Rows | StatCan | NSF |
|---|---|---|---|
| train | 6,041 | 5,486 | 555 |
| validation | 1,378 | 1,268 | 110 |
| test | 1,286 | 1,189 | 97 |
| total | 8,705 | 7,943 | 762 |
Schema
| Field | Type | Description |
|---|---|---|
id |
string | Original HiTab MD5 row identifier |
table_id |
string | Table file key (matches HiTab tables/hmt/) |
table_source |
string | statcan or nsf |
question |
string | Natural language question |
answer |
list[string] | Answer values (numbers and strings both as strings) |
aggregation |
list[string] | Aggregation operation(s) required |
sub_sentence |
string | Reference table-to-text sentence |
sentence_id |
string | HiTab sentence group identifier |
sub_sentence_id |
string | HiTab sub-sentence identifier |
linked_cells |
string (JSON) | Cell-to-entity/quantity linkage map |
answer_formulas |
list[string] | Spreadsheet-style formulas for answers |
reference_cells_map |
string (JSON) | Formula cell reference map |
table_title |
string | Full table title |
table_html |
string | Table rendered as HTML with hierarchical rowspan/colspan headers |
table_json |
string | Full table as JSON (title, top_root, left_root, data trees) |
Use table_source to filter to a single provenance if your licence
constraints require it.
Usage
from datasets import load_dataset
# All clean rows
ds = load_dataset("rootsautomation/HiTab-StatCan-NSF")
# Public-domain only (NSF)
nsf = ds.filter(lambda x: x["table_source"] == "nsf")
# StatCan only
statcan = ds.filter(lambda x: x["table_source"] == "statcan")
Citation
If you use this dataset please cite the original HiTab paper:
@inproceedings{cheng-etal-2022-hitab,
title = {{HiTab}: A Hierarchical Table Dataset for Question Answering
and Natural Language Generation},
author = {Cheng, Zhoujun and Dong, Haoyu and Wang, Zhiruo and Jia,
Ran and Guo, Jiaqi and Gao, Yan and Han, Shi and Lou, Jian-Guang
and Zhang, Dongmei},
booktitle = {Proceedings of the 60th Annual Meeting of the Association
for Computational Linguistics (Volume 1: Long Papers)},
year = {2022},
pages = {867--880},
}
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