alonsoapp/TABLET-Large

TABLET-Large

This is the Large sized train set of the TABLET dataset. It contains all train examples for all TABLET tasks, resulting in a total of 3,505,311 training examples across 17 tasks.
This dataset is self-contained, each example includes a table image, its HTML representation, and the associated task data.
However, if you're interested in downloading just the TABLET tables, check out TABLET-tables.

All TABLET Subsets:

• (train) TABLET-Small: The smallest TABLET subset, including 776,602 examples across 14 tasks.
• (train) TABLET-Medium: Includes all examples from TABLET-Small, plus Column Type Annotation, Entity Linking, and Relation Extraction tasks. Each task is capped at 140,000 examples, resulting in a total of 1,117,217 training examples across 17 tasks.
• (train) TABLET-Large: Includes all examples from TABLET-Medium with no cap on task size, resulting in a total of 3,505,311 training examples across 17 tasks.
• (dev) TABLET-dev: The development set of TABLET.
• (test) TABLET-test: The test set of TABLET.

For more information, see our paper, website, and GitHub repository.

Using the Dataset

Given its size, we recommend streaming the dataset instead of downloading it entirely to disk:

from datasets import load_dataset
dataset = load_dataset('alonsoapp/TABLET-Medium', split='fetaqa_train', streaming=True)
print(next(iter(dataset)))

Data Fields

Each sample within the dataset is structured with the following fields:

• example_id: Unique identifier for the example.
• task: The name of the task this example belongs to.
• src_example_ids: IDs of the original examples from the source dataset, formatted as {"Dataset name": "id"}. Use the get_original_example helper function from our published code to easily retrieve the source example.
• table_id: Unique identifier for the table.
• table_seed_id: ID referencing the table in its original (seed) dataset.
• table_seed_dataset: Name of the dataset where the table originated, typically matching the source dataset of the example.
• table_page_title: For tables sourced from Wikipedia, the corresponding page title.
• table_section_title: For Wikipedia tables, the title of the section where the table appears.
• table_variant: Either "raw" or "highlighted". Some examples visually highlight specific cells and this field indicates whether the table is unmodified (raw) or includes highlights (highlighted).
• img_source: Source of the table image. That is, whether the image comes from the Wikipedia visualization (wikipedia), a synthetic renderization from the data in the soruce dataset (seed_render), or directly copied from the original visualization of the table of the source dataset (PubTabNet, TabMWP).
• input: The instructified input used for training and evaluation (see paper). The input can be rephrased using information in metadata.
• output: The expected model output for the given input.
• split: Dataset split: train, dev, or test.
• metadata: Atomic data for the example to enable reconstruction or rephrasing of the instruction. Each key indicates a data element, the value can be obtained from either the 'input' or the 'output' strings using the substring defined by the character indexes in 'idx'. Use the get_metadata helper function from our published code to retrieve these values.
• table_wiki_page_id: For Wikipedia tables, the page ID corresponding to the article containing the table (useful for Wikipedia API queries).
• table_wiki_old_id: For Wikipedia tables, the “old ID” identifying the article version at the crawl time.
• table_html: HTML representation of the table. Use the render_table helper function from our code to render it in its original style. For highlighted variants, highlighted cells use the CSS class demeter_highlighted_cell. Remove any decorators for this class in the CSS to render identically to the raw version.
• table_img: The image representation of the table.

Citation

If you find TABLET useful in your research, please consider citing it by the following BibTeX entry.

@misc{alonso2025tabletlargescaledatasetrobust,
      title={TABLET: A Large-Scale Dataset for Robust Visual Table Understanding}, 
      author={Iñigo Alonso and Imanol Miranda and Eneko Agirre and Mirella Lapata},
      year={2025},
      eprint={2509.21205},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.21205}, 
}