license: other
task_categories:
- image-to-text
- table-structure-recognition
language:
- en
- zh
tags:
- table
- html
- image-to-html
- table-structure
- TABLET
TABLET
18642 table image/HTML pairs (17733 train, 909 test) synthesised from 9 table-question-answering source sets.
What is in here
TABLET_html_image_pairs.zip contains 18642 image/HTML pairs under train/ and
test/, one 437b41c90b28ac41e73230c221a05c1e.png and one 437b41c90b28ac41e73230c221a05c1e.html each. The HTML is the table
annotation; cells carry values, so this is a content-and-structure
annotation, not a structure-only one.
Source sets, and how many samples each contributed:
- TabFact: 12231
- TABMWP: 2381
- ToTTo: 1575
- WikiTableQuestions: 1146
- TAT-QA: 475
- WikiBIO: 363
- StatCan: 258
- InfoTabs: 187
- NSF: 26
PubTabNet is excluded on purpose: several table-recognition training mixes already contain it, and a benchmark that overlaps the training data does not measure generalisation.
Files
| file | what |
|---|---|
TABLET_html_image_pairs.zip |
the pairs (1.72 GB) |
TABLET_meta.jsonl |
one line per sample: split, name, seed, has_cell_values |
TABLET_stats.json |
counts and the seed breakdown |
TABLET_样本预览.html |
sample preview |
Splits
| split | n |
|---|---|
| train | 17733 |
| test | 909 |
Intended use
Table structure recognition and image-to-HTML generation. Score with TEDS when the annotation carries cell text; a structure-only metric is not meaningful here because the cells are not empty.