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Related Experiment Video

Updated: Jun 17, 2025

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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ENTRANT: A Large Financial Dataset for Table Understanding.

Elias Zavitsanos1, Dimitris Mavroeidis2, Eirini Spyropoulou2

  • 1Institute of Informatics and Telecommunications, National Centre for Scientific Research "Demokritos", Aghia Paraskevi, 15341, Greece. izavits@iit.demokritos.gr.

Scientific Data
|August 13, 2024
PubMed
Summary
This summary is machine-generated.

ENTRANT is a new financial dataset designed for training large models to understand tabular data. This structured dataset aids deep learning methods in table comprehension and downstream tasks like cell classification.

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Tabular data is crucial for organizing and comparing information in a two-dimensional matrix format.
  • Training large models to understand structured tables is essential for knowledge transfer in various applications.
  • Effective pre-training of these models necessitates large, well-formatted datasets that capture table and cell characteristics.

Purpose of the Study:

  • To introduce ENTRANT, a novel financial dataset specifically curated for pre-training deep learning models on table understanding.
  • To provide a machine-readable dataset with detailed table and cell information, including metadata, attributes, and hierarchical structure.
  • To facilitate automated data processing and validation for robust dataset utility.

Main Methods:

  • The ENTRANT dataset was created by transforming millions of financial tables.
  • Data processing and curation were fully automated.
  • Technical validation was performed using unit testing with high code coverage.

Main Results:

  • The ENTRANT dataset contains millions of transformed tables with cell attributes, positional, and hierarchical information.
  • The dataset is provided in a machine-readable format, including metadata.
  • Automated processing and validation ensure data quality and usability.

Conclusions:

  • ENTRANT is a valuable resource for advancing deep learning-based table understanding.
  • The dataset effectively supports pre-training tasks for models designed to interpret structured tabular data.
  • Demonstrated use in a pre-training task for cell classification highlights its practical applicability.