# ENTRANT: A Large Financial Dataset for Table Understanding > ENTRANT is a dataset of roughly 6.7 million financial tables extracted from SEC EDGAR filings (2013-2021), annotated with cell attributes, positional coordinates, and bi-tree hierarchies in JSON format to support table understanding model pre-training. - Authors: Hunter Heidenreich - Published: 2026-03-30 - Paper: Published (Scientific Data 2024) - Dataset: ENTRANT Dataset - Code: ENTRANT Code - Source: Bevaya Labs, --- ## TL;DR ENTRANT is a large-scale financial table dataset compiled from SEC EDGAR filings. It contains approximately 6.7 million tables extracted from roughly 330,000 XBRL-format reports filed between 2013 and 2021, covering ten report types including 10-K, 10-Q, and 8-K filings. Each table is stored as a JSON object with per-cell attributes (formatting, data type, formula references), spatial coordinates, and two explicit hierarchy trees encoding the top-down and left-right header structure. The authors demonstrate the dataset's usability by pre-training a TUTA model on ENTRANT and applying it to cell type classification, where it either matches or slightly improves on the original model depending on the training mix. --- ## What kind of paper is this? **Dominant:** $\Psi\_{\text{Resource}}$: the paper's central contribution is the dataset itself, including the automated crawling and transformation pipeline, the JSON annotation format with bi-tree hierarchies, and the public release on Zenodo. **Secondary:** $\Psi\_{\text{Evaluation}}$: the pre-training and downstream cell classification experiments serve as a technical validation of the dataset's usability rather than as a standalone evaluation contribution. --- ## What is the motivation? Table understanding covers a wide range of tasks: cell role prediction, table type classification, question answering over tables, schema mapping, and knowledge extraction. Most recent progress in this area relies on large pre-trained transformers (TaBERT, TaPas, TUTA, FORTAP) that consume millions of tables during pre-training. The datasets most commonly used for this purpose come from general-purpose web sources: Wikipedia tables, Common Crawl, and the WDC Web Table corpus. These sources require substantial preprocessing to extract tables and attach per-cell features in a format that models can consume directly. Financial tables from spreadsheets, which interleave textual and numerical content and commonly exhibit multi-level hierarchical header structures, are largely absent from existing public pre-training corpora. The one prior financial table resource cited by the authors (TAT-QA) contains only 20K tables, which is several orders of magnitude smaller than what large pre-training runs require. EDGAR, the SEC's public filing database, provides a continuously updated source of structured financial spreadsheets in XBRL format. Starting from 2009, SEC rules required publicly traded companies to tag every number and table in their filings with XBRL identifiers, making automated extraction reliable and consistent. The authors argue that this source, once processed, can fill the gap for large-scale, machine-readable financial tabular data. --- ## What is the novelty? ### Dataset scale and coverage ENTRANT covers approximately 331,448 reports filed between 2013 and 2021 across ten SEC form types. The 2013 start date reflects XBRL adoption maturity; the 2021 cutoff was chosen to align with prior research on financial misstatement detection, where average discovery lags of two to three years introduce noise in more recent filings. Processing these reports produced 6,735,407 tables distributed across roughly 330,000 JSON files. The dataset is not limited to annual financial statements. It includes quarterly reports (10-Q), current event reports (8-K), registration statements (S-1, S-4), foreign issuer reports (20-F, 18-K), and investment company filings (485BPOS, 497). This breadth means the vocabulary spans standard XBRL financial concepts (e.g., "Net Income", "Current Liabilities") as well as short textual disclosures and notes that accompany significant corporate events. ### JSON annotation format Each JSON file corresponds to one filing workbook and contains a list of table dictionaries. Each table stores: - Metadata: file description, language, and spreadsheet name - Table dimensions via a "RangeAddress" field - A "Cells" field structured as a list of row lists, where each cell is a dictionary Per-cell attributes include: | Key | Description | | :--- | :--- | | `T` | Cell value as string | | `V` | Cell value (typed) | | `is_header` | Participates in a header row | | `is_attribute` | Participates in a header column | | `coordinates` | [row index, column index] | | `HF` | Cell has a formula | | `A1` / `R1` | Absolute / relative formula cell reference | | `font_name`, `font_size` | Font metadata | | `wrap_text` | Whether text is wrapped within the cell dimension | | `BC`, `FC`, `FB`, `I` | Background color, font color, bold, italic flags | | `NS` | Number format | | `DT` | Data type | | `LB`, `TB`, `BB`, `RB` | Left, top, bottom, right border flags | | `O`, `HA`, `VA` | Orientation, horizontal alignment, vertical alignment | In addition to cell content, each table records merged regions and the counts of top header rows and left header columns. ### Bi-tree hierarchy representation A distinctive feature of ENTRANT is its explicit encoding of the two hierarchical structures present in financial tables. Financial spreadsheets routinely use merged cells and indentation to imply both a top-down hierarchy (column group headers spanning multiple time periods or categories) and a left-to-right hierarchy (row label groups organizing related financial line items). ENTRANT encodes both hierarchies as nested node dictionaries: ``` node = { 'RI': , 'CI': , 'Cd': [ , ... ] } ``` The root node uses coordinates (-1, -1) to indicate it lies conceptually outside the table grid. This representation, adopted from the TUTA paper's formalism, supports up to four levels of hierarchy depth, which the authors report covers all table types present in EDGAR filings. The two hierarchy trees are stored under the keys `TopTreeRoot` and `LeftTreeRoot` in each table dictionary. ### Automated, tested curation pipeline The crawling phase uses each company's Central Index Key (CIK) and EDGAR accession number to construct API endpoints pointing to `Financial_Report.xlsx` files. Reports without valid XBRL tags are filtered out before downloading. The transformation phase uses OpenPyXL to extract tables from each workbook, annotate cells with the attributes above, and construct the bi-tree structures. The paper states that a test suite of 45 unit tests covering all transformation steps passed successfully at the time of publication. A post-processing pass verifies that no empty rows or columns remain in any extracted table, and all JSON files are validated by loading them into Python dictionaries. --- ## What experiments were performed? The authors pre-trained TUTA, a transformer-based table understanding model, on two data configurations to validate that ENTRANT is correctly formatted and consumable: 1. **TUTA (WikiTables + WDC + ENTRANT):** pre-trained on the three public datasets combined. 2. **TUTA (ENTRANT only):** pre-trained exclusively on ENTRANT. Both models were then evaluated on cell type classification using two datasets from the DECO collection (Koci et al., ICDAR 2019), DeEx and SAUS, which annotate cells into six types: "metadata", "notes", "data", "top attribute", "left attribute", and "derived". These datasets draw from financial, educational, and public health domains. Results are reported as Macro F1: | Model | DeEx | SAUS | | :--- | :--- | :--- | | TUTA original (WikiTables + WDC + non-public spreadsheets) | 76.6 | 90.2 | | TUTA (WikiTables + WDC + ENTRANT) | 78.8 | 91.9 | | TUTA (ENTRANT only) | 74.2 | 88.4 | The model pre-trained only on ENTRANT performs approximately 2 Macro F1 points below the original TUTA baseline (2.4 on DeEx, 1.8 on SAUS). When ENTRANT is combined with the other two public datasets, the resulting model slightly outperforms the original baseline on both tasks. --- ## What are the outcomes/conclusions? The results suggest that ENTRANT can serve as a drop-in addition to existing public pre-training corpora for table understanding models. The ENTRANT-only model performs reasonably well despite being restricted to a single financial domain, which the authors attribute to the dataset's vocabulary breadth (financial statements contain diverse numeric formats, date references, and conceptual labels) and its explicit structural annotations. The paper notes several limitations worth considering: - The dataset covers only filings from 2013 to 2021. More recent EDGAR filings are not included and would need to be crawled using the released code. - The 2021 cutoff introduces a specific bias: filings close to the cutoff are less likely to have had misstatements discovered, which was the original research context but has no direct bearing on table understanding. - Tables below a minimum size threshold and tables with sparse or broken content are filtered out during preprocessing. The exact filter criteria are described qualitatively but not as precise thresholds. - The ENTRANT-only pre-training experiment uses downstream datasets (DeEx, SAUS) that include financial tables, which may partially favor a financial-domain pre-training corpus. Generalization to other domains is not directly measured. - The paper does not report statistical confidence intervals or multiple pre-training runs, so the observed improvements over the TUTA baseline should be interpreted cautiously. The code release allows users to extend the dataset by crawling more recent EDGAR filings or to apply the transformation pipeline to other XBRL-format corporate spreadsheets. --- ## Reproducibility ### Models - **TUTA**: tree-based transformer for general table pre-training; originally described by Wang et al. (KDD 2021). The pre-training uses all three objectives described in the original TUTA paper. Model weights from the published TUTA codebase (https://github.com/microsoft/TUTA_table_understanding) were used as the starting point. Parameter count and layer configuration are not restated in the ENTRANT paper; readers should consult the original TUTA paper. ### Algorithms - Pre-training followed the TUTA procedure using the released Microsoft code. No modifications to the TUTA training recipe are described. - Downstream cell type classification uses the fine-tuning setup from the original TUTA paper. - Specific optimizer, learning rate, batch size, and compute budget for the ENTRANT pre-training runs are not reported. ### Data - **Source:** SEC EDGAR public filings, accessed via the EDGAR API. The underlying financial data is public domain. Spreadsheets are in XBRL-annotated XLSX format. - **Coverage:** 2013-2021, ten report types, approximately 331,448 reports, 6,735,407 tables. - **File organization:** One JSON file per company filing, organized in subfolders by report type. Each subfolder is provided as a zip archive on Zenodo. - **File naming convention:** `___.json` - **Dataset availability:** Released on Zenodo at https://doi.org/10.5281/zenodo.10667088 under CC-BY-4.0 (open access, 17.3 GB total across 10 zip archives, one per report type). Note that the dataset license (CC-BY-4.0) is more permissive than the published article license (CC-BY-NC-ND-4.0). - **Code availability:** Python scripts for crawling and transformation released at https://github.com/iit-Demokritos/entrant under CC-BY-4.0. - **Processing libraries:** OpenPyXL for spreadsheet parsing. ### Evaluation - **Metric:** Macro F1 for multi-class cell type classification (six classes: metadata, notes, data, top attribute, left attribute, derived). - **Downstream datasets:** DeEx and SAUS from the DECO collection (Koci et al., ICDAR 2019), covering financial, educational, and public health tables. - **Baselines:** Original TUTA model pre-trained on WikiTables, WDC, and non-public web-crawled spreadsheets. This baseline uses pre-training data that is not fully reproducible by external parties. - No error bars, significance tests, or multiple pre-training seeds are reported. ### Hardware - Computing and storage resources were provided by the SKEL AI Lab at NCSR "Demokritos". Specific GPU types, GPU counts, pre-training duration, and memory requirements are not reported in the paper. --- ## BibTeX ```bibtex @article{zavitsanos2024entrant, title={ENTRANT: A Large Financial Dataset for Table Understanding}, author={Zavitsanos, Elias and Mavroeidis, Dimitris and Spyropoulou, Eirini and Fergadiotis, Manos and Paliouras, Georgios}, journal={Scientific Data}, volume={11}, pages={876}, year={2024}, publisher={Nature Publishing Group}, doi={10.1038/s41597-024-03605-5} } ```