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--- |
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license: other |
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pretty_name: TabLib |
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size_categories: |
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- 100M<n<1B |
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extra_gated_prompt: >- |
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Accessing the full TabLib dataset requires permission from Approximate Labs. To request access, please provide the following details and Approximate Labs will process your request as soon as possible. |
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extra_gated_fields: |
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Full Name: text |
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Email: text |
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Country: text |
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Organization: text |
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What do you intend to do with this dataset?: text |
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I agree to abide by the license requirements of the data contained in TabLib: checkbox |
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--- |
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[](https://discord.gg/kW9nBQErGe) |
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<img src="https://approximatelabs.com/tablib.png" width="800" /> |
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# TabLib |
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A minimally-preprocessed dataset of 627M tables (69 TiB) extracted from HTML, PDF, CSV, TSV, Excel, and SQLite files from GitHub and Common Crawl. |
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This includes 867B tokens of "context metadata": each table includes provenance information and table context such as filename, text before/after, HTML metadata, etc. |
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A smaller 0.1% sample of this dataset can be found [here](https://huggingface.co/datasets/approximatelabs/tablib-v1-sample). |
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For more information, read the [paper](https://arxiv.org/abs/2310.07875) & [announcement blog](https://approximatelabs.com/blog/tablib). |
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# Dataset Details |
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## Sources |
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* **GitHub**: nearly all public GitHub repositories |
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* **Common Crawl**: the `CC-MAIN-2023-23` crawl |
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## Reading Tables |
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Tables are stored as serialized Arrow bytes in the `arrow_bytes` column. To read these, you will need to deserialize the bytes: |
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```python |
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import datasets |
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import pyarrow as pa |
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# load a single file of the dataset |
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ds = datasets.load_dataset( |
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'approximatelabs/tablib-v1-full', |
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data_files='tablib/job=github_000005/batch=000001/part=000001/manifest.parquet', |
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token='...', |
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) |
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df = ds['train'].to_pandas() |
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tables = [pa.RecordBatchStreamReader(b).read_all() for b in df['arrow_bytes']] |
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``` |
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## Licensing |
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This dataset is intended for research use only. |
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For specific licensing information, refer to the license of the specific datum being used. |
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# Contact |
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If you have any questions, comments, or concerns about licensing, pii, etc. please contact using [this form](https://forms.gle/C74VTWP7L78QDVR67). |
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# Approximate Labs |
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TabLib is a project from Approximate Labs. Find us on [Twitter](https://twitter.com/approximatelabs), [Github](https://github.com/approximatelabs), [Linkedin](https://www.linkedin.com/company/approximate-labs), and [Discord](https://discord.gg/kW9nBQErGe). |
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# Citations |
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If you use TabLib for any of your research, please cite the TabLib paper: |
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``` |
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@misc{eggert2023tablib, |
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title={TabLib: A Dataset of 627M Tables with Context}, |
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author={Gus Eggert and Kevin Huo and Mike Biven and Justin Waugh}, |
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year={2023}, |
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eprint={2310.07875}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
