metadata

license: other
pretty_name: TabLib
size_categories:
  - 1M<n<10M
extra_gated_prompt: >-
  Access to this dataset is automatically granted once this form is completed.

  Note that this access request is for the TabLib sample, not [the full TabLib
  dataset](https://huggingface.co./datasets/approximatelabs/tablib-v1-full).
extra_gated_fields:
  I agree to abide by the license requirements of the data contained in TabLib: checkbox

TabLib Sample

NOTE: This is a 0.1% sample of the full TabLib dataset.

TabLib is a minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl.

This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc.

For more information, read the paper & announcement blog.

Dataset Details

Sources

GitHub: nearly all public GitHub repositories
Common Crawl: the CC-MAIN-2023-23 crawl

Reading Tables

Tables are stored as serialized Arrow bytes in the arrow_bytes column. To read these, you will need to deserialize the bytes:

import datasets
import pyarrow as pa

# load a single file of the dataset
ds = datasets.load_dataset(
    'approximatelabs/tablib-v1-sample',
    token='...',
)

df = ds['train'].to_pandas()

tables = [pa.RecordBatchStreamReader(b).read_all() for b in df['arrow_bytes']]

Licensing

This dataset is intended for research use only.

For specific licensing information, refer to the license of the specific datum being used.

Contact

If you have any questions, comments, or concerns about licensing, pii, etc. please contact using this form.

Approximate Labs

TabLib is a project from Approximate Labs. Find us on Twitter, Github, Linkedin, and Discord.

Citations

If you use TabLib for any of your research, please cite the TabLib paper:

@misc{eggert2023tablib,
      title={TabLib: A Dataset of 627M Tables with Context}, 
      author={Gus Eggert and Kevin Huo and Mike Biven and Justin Waugh},
      year={2023},
      eprint={2310.07875},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}