README.md

---
dataset_info:
  features:
  - name: example_id
    dtype: string
  - name: task
    dtype:
      class_label:
        names:
          '0': col_type
          '1': ent_link
          '2': fetaqa
          '3': hitab
          '4': hybridqa
          '5': infotabs
          '6': merged_cell_detection
          '7': rel_extraction
          '8': row_column_extraction
          '9': struct_aware_parse
          '10': tabfact
          '11': table_cell_extraction
          '12': table_cell_location
          '13': table_instruction
          '14': table_recognition
          '15': table_size_detection
          '16': tabmwp
          '17': tat-qa
          '18': totto
          '19': wikibio
          '20': wikitq
  - name: src_example_ids
    dtype: string
  - name: table_id
    dtype: string
  - name: table_seed_id
    dtype: string
  - name: table_seed_dataset
    dtype: string
  - name: table_page_title
    dtype: string
  - name: table_section_title
    dtype: string
  - name: table_variant
    dtype: string
  - name: img_source
    dtype:
      class_label:
        names:
          '0': PubTabNet
          '1': TABMWP
          '2': seed_render
          '3': wikipedia
  - name: input
    dtype: large_string
  - name: output
    dtype: string
  - name: split
    dtype:
      class_label:
        names:
          '0': dev
          '1': test
          '2': train
  - name: metadata
    dtype: string
  - name: table_wiki_page_id
    dtype: string
  - name: table_wiki_old_id
    dtype: string
  - name: table_html
    dtype: large_string
  - name: table_img
    dtype: image
  splits:
  - name: wikibio_train
    num_bytes: 11073920562
    num_examples: 140000
  - name: tatqa_train
    num_bytes: 114858299
    num_examples: 2201
  - name: hybridqa_train
    num_bytes: 14932215002
    num_examples: 62670
  - name: table_instruction_train
    num_bytes: 12180312819
    num_examples: 136944
  - name: tabmwp_train
    num_bytes: 326184930
    num_examples: 23059
  - name: hitab_train
    num_bytes: 1026591527
    num_examples: 7417
  - name: table_recognition_train
    num_bytes: 788489258
    num_examples: 6927
  - name: table_cell_extraction_train
    num_bytes: 2102295132
    num_examples: 7727
  - name: table_size_detection_train
    num_bytes: 1695496703
    num_examples: 7800
  - name: merged_cell_detection_train
    num_bytes: 993975274
    num_examples: 7500
  - name: table_cell_location_train
    num_bytes: 1227016660
    num_examples: 7708
  - name: tabfact_train
    num_bytes: 10356555069
    num_examples: 87717
  - name: totto_train
    num_bytes: 36076773336
    num_examples: 110934
  - name: row_column_extraction_train
    num_bytes: 2261383645
    num_examples: 7721
  - name: infotabs_train
    num_bytes: 1486818637
    num_examples: 16538
  - name: wikitq_train
    num_bytes: 3661437235
    num_examples: 14152
  - name: struct_aware_parse_train
    num_bytes: 2711066295
    num_examples: 126581
  - name: fetaqa_train
    num_bytes: 366072076
    num_examples: 3006
  download_size: 97565644868
  dataset_size: 103381462459
configs:
- config_name: default
  data_files:
  - split: wikibio_train
    path: data/wikibio_train-*
  - split: tatqa_train
    path: data/tatqa_train-*
  - split: hybridqa_train
    path: data/hybridqa_train-*
  - split: table_instruction_train
    path: data/table_instruction_train-*
  - split: tabmwp_train
    path: data/tabmwp_train-*
  - split: hitab_train
    path: data/hitab_train-*
  - split: fetaqa_train
    path: data/fetaqa_train-*
  - split: table_recognition_train
    path: data/table_recognition_train-*
  - split: table_cell_extraction_train
    path: data/table_cell_extraction_train-*
  - split: table_size_detection_train
    path: data/table_size_detection_train-*
  - split: merged_cell_detection_train
    path: data/merged_cell_detection_train-*
  - split: table_cell_location_train
    path: data/table_cell_location_train-*
  - split: tabfact_train
    path: data/tabfact_train-*
  - split: totto_train
    path: data/totto_train-*
  - split: row_column_extraction_train
    path: data/row_column_extraction_train-*
  - split: infotabs_train
    path: data/infotabs_train-*
  - split: wikitq_train
    path: data/wikitq_train-*
  - split: struct_aware_parse_train
    path: data/struct_aware_parse_train-*
---
### TABLET-Small

This is the _Small_ sized **train set** of the **TABLET** dataset. It contains the train examples for 14 **TABLET** tasks.  
Each task is capped at **140,000 examples**, resulting in a total of **776,602 training examples** across **14 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](https://huggingface.co/datasets/alonsoapp/TABLET-tables).  

All TABLET Subsets:
- _(train)_ [**TABLET-Small**](https://huggingface.co/datasets/alonsoapp/TABLET-Small): The smallest TABLET subset, including **776,602 examples** across **14 tasks**.
- _(train)_ [**TABLET-Medium**](https://huggingface.co/datasets/alonsoapp/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**](https://huggingface.co/datasets/alonsoapp/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**](https://huggingface.co/datasets/alonsoapp/TABLET-dev): The **development** set of TABLET.
- _(test)_ [**TABLET-test**](https://huggingface.co/datasets/alonsoapp/TABLET-test): The **test** set of TABLET.

For more information, see our [paper](https://arxiv.org/pdf/2509.21205), [website](https://precious-panda-5ce815.netlify.app/tablet/), and [GitHub repository](https://github.com/AlonsoApp/TABLET). 

#### Using the Dataset

Given its size, we recommend [streaming](https://huggingface.co/docs/datasets/stream) the dataset instead of downloading it entirely to disk:

```python
from datasets import load_dataset
dataset = load_dataset('alonsoapp/TABLET-Small', 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](https://github.com/AlonsoApp/TABLET) 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](https://arxiv.org/pdf/2509.21205)). 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](https://github.com/AlonsoApp/TABLET) 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](https://github.com/AlonsoApp/TABLET) 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.

```bibtex
@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}, 
}
```