| --- |
| 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. |
|
|
