---
title: ledger-long-context-multi-kpi
canonical_url: "https://www.modelscope.cn/datasets/artefactory/ledger-long-context-multi-kpi"
md_url: "https://www.modelscope.cn/datasets/artefactory/ledger-long-context-multi-kpi.md"
repository: artefactory/ledger-long-context-multi-kpi
last_updated: 2026-07-17
license: cc-by-4.0
storage_size: "3.7 GB"
downloads: 586
stars: 0
---

# ledger-long-context-multi-kpi

> ledger-long-context-multi-kpi - artefactory 在 ModelScope 开源的数据集。the LEDGER Long-Context Multi-KPI extraction datasets and benchmarks.

artefactory/ledger-long-context-multi-kpi 是 ModelScope 魔搭社区上的数据集，存储大小 3.7 GB，采用 cc-by-4.0 许可。

- **Repository**: artefactory/ledger-long-context-multi-kpi
- **License**: cc-by-4.0
- **Storage size**: 3.7 GB
- **Downloads**: 586
- **Stars**: 0
- **Last updated**: 2026-07-17

Source: https://www.modelscope.cn/datasets/artefactory/ledger-long-context-multi-kpi

---

# the LEDGER Long-Context Multi-KPI extraction datasets and benchmarks.

OCR'd annual reports with ground-truth KPI values for financial information extraction benchmarking.

## Dataset Description

This dataset pairs OCR-extracted annual report text (from DeepSeek OCR) with structured KPI ground-truth values. It is designed for evaluating LLM-based financial information extraction, retrieval, and needle-in-a-haystack tasks.

### Configs

| Config | Reports | Companies | KPI rows | Years | Purpose |
|--------|---------|-----------|----------|-------|---------|
| `no_eval` | 4,505 | 725 | 104,529 | 2009–2024 | Training / development |
| `eval` | 494 | 111 | 13,519 | 2017–2022 | Benchmark evaluation |

### Schema

Each row in the parquet files contains:

| Column | Type | Description |
|--------|------|-------------|
| `ticker` | string | Stock ticker symbol |
| `exchange` | string | Stock exchange (NYSE, NASDAQ, LSE, AMEX, ASX, OTC) |
| `company_name` | string | Company long name |
| `industry` | string | Industry classification |
| `year` | int | Fiscal year |
| `revenue` | float64 | Total revenue |
| `net_income` | float64 | Net income |
| `total_assets` | float64 | Total assets |
| `total_liabilities` | float64 | Total liabilities |
| ... | float64 | 31 KPI columns total (see below) |
| `mmd_text` | string | Full OCR text of the annual report (Markdown with page splits) |

**KPI columns (31):** `accounts_payable`, `accounts_receivable`, `capex`, `cash_and_equivalents`, `cash_incl_restricted`, `cost_of_revenue`, `depreciation_amortization`, `dividends_paid`, `eps_basic`, `eps_diluted`, `financing_cash_flow`, `gross_profit`, `income_tax_expense`, `interest_expense`, `inventory`, `investing_cash_flow`, `long_term_debt_current`, `long_term_debt_noncurrent`, `long_term_debt_total`, `net_income`, `operating_cash_flow`, `operating_income`, `rd_expense`, `revenue`, `sga_expense`, `shares_outstanding`, `short_term_borrowings`, `stockholders_equity`, `stockholders_equity_incl_nci`, `total_assets`, `total_liabilities`.

KPI values are in millions (as-reported, no FX conversion). NaN means the KPI was not available for that report/year.

### Additional Files

- `no_eval/mmd/` and `eval/mmd/`: Raw `.mmd` files (same text as the `mmd_text` column, for direct file access).
- `eval/images/`: Page-level JPEG images for eval reports (not included for no_eval to save space).

### OCR Format

The `.mmd` files use Markdown with page boundaries marked by `<--- Page Split --->`. Images are referenced as `![](images/{page}_{idx}.jpg)`.

## Usage

```python
from datasets import load_dataset

# Load training set
ds = load_dataset("artefactory/ledger-long-context-multi-kpi", "no_eval")

# Load eval set
ds_eval = load_dataset("artefactory/ledger-long-context-multi-kpi", "eval")

# Example: filter to reports with revenue data
ds_filtered = ds["train"].filter(lambda x: x["revenue"] is not None)
```

## Data Sources

- **OCR text**: DeepSeek OCR applied to annual report PDFs from SEC EDGAR, LSE, ASX, and other exchanges.
- **KPI values**: SEC EDGAR (XBRL companyfacts) for US listings; yfinance for non-US; Alpha Vantage for gap-fill.

## Citation key

If you use LEDGER datasets and / or code ressources, please consider citing our work with:

```
@misc{moslonka2026ledgerlongcontextbenchmarkcorporate,
      title={LEDGER: A Long-Context Benchmark of Corporate Annual Reports for Grounded Financial Retrieval and Extraction}, 
      author={Charles Moslonka and Amaury de Vitry and Arthur Garnier and Hicham Randrianarivo and Emmanuel Malherbe},
      year={2026},
      eprint={2606.13100},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.13100}, 
}
```

## Links

- **Collection**: [artefactory/ledger](https://huggingface.co/collections/artefactory/ledger)
- **Code**: [github.com/artefactory/LEDGER](https://github.com/artefactory/LEDGER)
- **ArXiv** [https://arxiv.org/abs/2606.13100](https://arxiv.org/abs/2606.13100)