---
license: cc-by-4.0
language:
- en
task_categories:
- table-question-answering
- text-classification
pretty_name: ENTRANT
tags:
- financial
- sec-edgar
- tables
- annual-report
- structured-data
configs:
- config_name: "18-K"
  data_files:
  - split: train
    path: 18-K/train-*.parquet
- config_name: "485BPOS"
  data_files:
  - split: train
    path: 485BPOS/train-*.parquet
- config_name: "497"
  data_files:
  - split: train
    path: 497/train-*.parquet
- config_name: "10-KT"
  data_files:
  - split: train
    path: 10-KT/train-*.parquet
- config_name: "S-1"
  data_files:
  - split: train
    path: S-1/train-*.parquet
- config_name: "8-K"
  data_files:
  - split: train
    path: 8-K/train-*.parquet
- config_name: "20-F"
  data_files:
  - split: train
    path: 20-F/train-*.parquet
- config_name: "S-4"
  data_files:
  - split: train
    path: S-4/train-*.parquet
- config_name: "10-K"
  data_files:
  - split: train
    path: 10-K/train-*.parquet
- config_name: "10-Q"
  data_files:
  - split: train
    path: 10-Q/train-*.parquet
---

# ENTRANT

A HuggingFace mirror of the [ENTRANT](https://zenodo.org/records/10667088) dataset
(Zenodo record 10667088, CC-BY-4.0): 6.7 million structured financial tables
extracted from ~330,000 SEC EDGAR filings spanning 10 filing types.

Original paper: Gialitsis et al., *Scientific Data* 2024,
[10.1038/s41597-024-03605-5](https://doi.org/10.1038/s41597-024-03605-5).

## License

CC-BY-4.0 (dataset annotations and structural metadata).

The underlying source documents are SEC EDGAR filings -- publicly filed
corporate documents. The factual content of financial statements is not
copyrightable under *Feist v. Rural Telephone Service* (1991); the
Nature Scientific Data publication provides strong institutional backing
for the CC-BY-4.0 claim on this corpus.

Cite the original ENTRANT paper if you use this dataset (see Citation below).

## Configs

One HuggingFace config per SEC filing type. Each config is a single `train`
split (the authors did not define evaluation splits; partition as needed).

| Config | Filing type | Description |
|--------|-------------|-------------|
| `10-K` | Annual report | Large accelerated filers, ~5.9 GB ZIP |
| `10-Q` | Quarterly report | Most common filing, ~9.4 GB ZIP |
| `20-F` | Foreign annual report | Non-US issuers, ~556 MB ZIP |
| `S-1` | IPO registration | Initial public offerings, ~179 MB ZIP |
| `S-4` | Merger/acquisition | M&A registrations, ~886 MB ZIP |
| `8-K` | Current report | Material events, ~347 MB ZIP |
| `497` | Mutual fund prospectus | Investment companies, ~29 MB ZIP |
| `485BPOS` | Fund registration | Post-effective amendments, ~19 MB ZIP |
| `10-KT` | Transition annual report | Fiscal-year change filers, ~18 MB ZIP |
| `18-K` | Foreign government annual | Sovereign / foreign government, ~37 KB ZIP |

## Schema

| Field | Type | Description |
|-------|------|-------------|
| `table_id` | string | `{cik}_{accession_number}_{table_index}` |
| `filing_type` | string | SEC filing type (e.g. `10-K`) |
| `cik` | string | SEC Central Index Key |
| `filing_year` | string | Year of filing |
| `accession_number` | string | SEC accession number |
| `title` | string | Table title (may be empty) |
| `n_rows` | int32 | Number of rows (from RangeAddress) |
| `n_cols` | int32 | Number of columns (from RangeAddress) |
| `n_header_rows` | int32 | Number of top header rows |
| `n_header_cols` | int32 | Number of left header columns |
| `html` | string | Table rendered as HTML with rowspan/colspan |
| `cells` | list[struct] | Per-cell records (anchor cells only; see below) |
| `language` | string | Language of content |

**Cell struct fields:** `row`, `col`, `rowspan`, `colspan` (int32);
`text` (string); `is_header` (bool, top header); `is_attribute` (bool, left header).
Only anchor cells (top-left corner of merged spans) are included;
interior cells of a merged region are omitted.

## Usage

```python
from datasets import load_dataset

# Stream annual reports without downloading all 17 GB
ds = load_dataset(
    "rootsautomation/ENTRANT",
    "10-K",
    split="train",
    streaming=True,
)
for row in ds:
    print(row["title"], row["n_rows"], row["n_cols"])
    break

# Load a smaller config fully into memory
ds_18k = load_dataset("rootsautomation/ENTRANT", "18-K", split="train")
```

## Citation

```bibtex
@article{gialitsis2024entrant,
  title   = {ENTRANT: A Large Financial Dataset for Table Understanding},
  author  = {Gialitsis, Nikolaos and Tzoumerkas, Konstantinos
             and Dalamagas, Theodore},
  journal = {Scientific Data},
  volume  = {11},
  year    = {2024},
  doi     = {10.1038/s41597-024-03605-5},
}
```