Mirror pinned upstream RuTaR with Parquet and source attribution

.gitattributes

@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+original/rutar.xlsx filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+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.

data/test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da6cf232b3b513ef37284b6c5f7f807e6e77e643235a4006d62cb3ee25b4de8e
+size 425075

manifest.json

@@ -0,0 +1,14 @@
+{
+  "repository": "RefalMachine/RuTaR",
+  "upstream_repository": "https://github.com/rutar-anonymous/RuTaR",
+  "upstream_revision": "76c6ef0cafe89fe1b400b673478066108b0493ac",
+  "upstream_filename": "rutar.xlsx",
+  "upstream_sha256": "5ce0824ea3d806c2475ac2d1880cfd1795a34ff1ac31a65b94a14d75cd136dc7",
+  "row_count": 209,
+  "filename": "data/test.parquet",
+  "parquet_sha256": "da6cf232b3b513ef37284b6c5f7f807e6e77e643235a4006d62cb3ee25b4de8e",
+  "sources_sha256": "085c20a0febef97f9e12ec86dd5ad1381ea06aa858b8d6542f11faffad24bb6c",
+  "source_count": 480,
+  "format_version": "rutar_lossless_parquet_v1",
+  "pyarrow_version": "25.0.1"
+}

original/rutar.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ce0824ea3d806c2475ac2d1880cfd1795a34ff1ac31a65b94a14d75cd136dc7
+size 252051