---
language:
- ru
task_categories:
- text-classification
tags:
- legal
- taxes
- reasoning
pretty_name: RuTaR (Russian Tax Reasoning)
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test.parquet
---

# RuTaR — Russian Tax Reasoning

This is a format mirror of [rutar-anonymous/RuTaR](https://github.com/rutar-anonymous/RuTaR), attributed to
its upstream authors. It contains Russian tax questions derived from letters
of the Russian Ministry of Finance and Federal Tax Service. Original binary
labels are preserved: **1 = yes, 0 = no**.

## Original source and attribution

- [Upstream repository and documentation](https://github.com/rutar-anonymous/RuTaR)
- [Exact upstream commit](https://github.com/rutar-anonymous/RuTaR/tree/76c6ef0cafe89fe1b400b673478066108b0493ac)
- [Original workbook](https://github.com/rutar-anonymous/RuTaR/blob/76c6ef0cafe89fe1b400b673478066108b0493ac/rutar.xlsx)
- [Original reference-source corpus](https://github.com/rutar-anonymous/RuTaR/blob/76c6ef0cafe89fe1b400b673478066108b0493ac/sources_dataset_for_rutar.json)
- Individual government-letter links are retained in `source_url`, with
  document titles, dates and issuing body in each record. One source URL is missing upstream.

The upstream snapshot does not include a license file or a license statement.
This mirror does not assert or grant a new license; consult the original
repository/authors for reuse terms. No author identity, paper citation or
license has been inferred from the anonymous repository name.

## Files and preservation

- `data/test.parquet`: all **209** nonempty workbook records, in original order.
- `original/rutar.xlsx`: byte-for-byte original workbook (SHA-256 `5ce0824ea3d806c2475ac2d1880cfd1795a34ff1ac31a65b94a14d75cd136dc7`).
- `sources/sources_dataset_for_rutar.json`: byte-for-byte original RAG corpus,
  **480** citation-to-text entries.
- `manifest.json`: upstream revision, file checksums and conversion version.

`test` is a packaging split for the complete upstream table; upstream supplies
no train/test split. No questions, duplicates or gold labels are removed here.
Blank question at ID `119` and duplicate questions at IDs `114`/`117` remain
available for auditing. Empty worksheet rows are omitted.

The unnamed workbook index becomes a stable string `id`. `true_answer` becomes
an integer 0/1. Other original columns remain strings with blank cells represented
as null. `found_sources` retains its original string representation of a list.
Dates remain original strings. Original XLSX is retained for exact source recovery.

## Columns

| Column | Meaning |
|---|---|
| `id` | Original workbook numeric index, represented as a string |
| `title` | Government document title |
| `date_publication` | Original document publication date |
| `full_text` | Full original letter, including its answer |
| `question_letter` / `answer_letter` | Question/answer parsed from the letter |
| `letter_type` | Issuing body (`minfin` or Federal Tax Service identifier) |
| `source_url` | Link to the original government document |
| `question_for_llm` | Upstream prepared yes/no question (one null) |
| `found_sources` | Original string list of references; texts are in the source corpus |
| `true_answer` | Gold: 1 yes, 0 no |

## Loading

```python
from datasets import load_dataset
rows = load_dataset("RefalMachine/RuTaR", split="test")
```

Pin `revision` to a Hub commit for reproducible evaluation. HF caches the Parquet
snapshot locally, so ordinary repeated runs reuse downloaded files.

## Evaluation considerations

The full letters and their parsed answers contain evidence used to author the
questions; feeding them into a closed-book prompt leaks gold information.
For closed-book evaluation, use only `question_for_llm` plus an explicit answer
instruction, and keep demonstration questions/documents separate from evaluation.
LLMTF reserves IDs 0–4 as a fixed demonstration pool, drops missing question 119
and duplicate 117, and evaluates 202 questions for both zero-shot and five-shot.
That is an LLMTF protocol, not an upstream split or a reproduction of upstream RAG.

Labels refer to the source documents and their historical dates; they have not
been rewritten to reflect current tax law. This mirror preserves upstream
annotations and does not add legal expert validation.