Datasets:

rafaelsouza-tech
/

rag-eval-ptbr

pretty_name: rag-eval-ptbr — Brazilian tax-law RAG evaluation golden set
license: cc-by-4.0
language:
  - pt
task_categories:
  - question-answering
  - text-retrieval
tags:
  - rag
  - rag-evaluation
  - llm-as-judge
  - legal
  - tax
  - brazil
  - portuguese
size_categories:
  - n<1K
configs:
  - config_name: tributario-golden
    data_files:
      - split: test
        path: tributario-golden/test.parquet
  - config_name: tributario-corpus
    data_files:
      - split: test
        path: tributario-corpus/test.parquet

rag-eval-ptbr — golden set tributário (pt-BR)

End-to-end RAG evaluation golden set in Brazilian Portuguese, tax-law domain (CTN — National Tax Code — and LC 123/2006, Simples Nacional). Designed to measure faithfulness/groundedness, context precision/recall, retrieval quality and citation accuracy of RAG pipelines — not a reading-comprehension QA set.

Built for (and consumed by) the open-source harness rag-eval-ptbr, which adds a statistically grounded regression runner (rageval run / rageval compare).

Versão em português abaixo.

Configs

Config	Rows	What it is
`tributario-golden`	35	evaluation items (split `test`)
`tributario-corpus`	536	canonical article-level corpus with stable semantic IDs

Golden item fields: id, question (natural question, as a taxpayer/lawyer would ask), reference_answer (curated, fully supported by the labeled passages), golden_passage_ids (complete and minimal set of supporting corpus chunks), reference_citations (canonical format (CTN, art. 173, I)), answerable (4 items are deliberately unanswerable from the corpus — abstention probes) and metadata (question type: factual / multi_hop / condicional / temporal / nao_respondivel; difficulty; source law; smoke-set flag; curation provenance).

Corpus chunk fields: doc_id (semantic, hierarchical, stable — ctn:art-173, lc123:art-18-p4), text (verbatim official text), source_law, article, url. Scope: full CTN (arts. 1–218) + LC 123/2006 (arts. 1–41). Retrieval metrics (recall@k, MRR, nDCG) are deterministic doc_id matching — no LLM needed.

Construction & curation

Corpus parsed verbatim from official Planalto sources by a reproducible script (scripts/build_corpus.py in the repo); articles over ~350 tokens subdivided by paragraph.
Items LLM-generated grounded on real corpus passages, by a model from a different family than the judge (against self-preference bias).
Adversarial verification, one independent pass per item: support of every answer claim checked against the actual chunk text, completeness/minimality of golden_passage_ids, citation format, question naturalness. Items were fixed or dropped.
Deduplication and consolidation (36 → 35 items).

Honest provenance: curated_by reads geração sintética por LLM (verificação adversarial; revisão humana pendente). Item-by-item human review is the acceptance bar for v1.0; this v0.x release is published for reproducibility and early use.

Limitations

Single domain (federal tax law); 35 items — designed for paired regression testing, not leaderboard-scale comparison.
Reference answers reflect the statute text as compiled in July 2026; they ignore case law by design (answers must be fully supported by the corpus).
Generated by an LLM: despite adversarial verification, residual errors are possible — open an issue in the repo if you find one.
Not legal advice.

License & sources

Source texts are official Brazilian normative acts, in the public domain (Law 9.610/1998, art. 8, IV). The curation/annotation layer (questions, reference answers, labels) is released under CC-BY-4.0. Attribution: Rafael Souza, rag-eval-ptbr golden set tributário.

Citation

@misc{souza2026rageval,
  author = {Souza, Rafael},
  title = {rag-eval-ptbr: RAG evaluation harness and golden set for Brazilian Portuguese},
  year = {2026},
  url = {https://huggingface.co/datasets/rafaelsouza-tech/rag-eval-ptbr}
}

Versão em português

Golden set de avaliação de RAG ponta a ponta em português brasileiro, domínio tributário (CTN e LC 123/2006). Feito para medir faithfulness/groundedness, context precision/recall, qualidade de retrieval e acurácia de citação de pipelines RAG — não é um QA set de compreensão de leitura.

Construção: corpus verbatim das fontes oficiais do Planalto (script reprodutível); itens gerados por um LLM ancorados em passagens reais; verificação adversarial item a item; deduplicação (36 → 35). Proveniência honesta: revisão humana item a item ainda pendente (barra de aceitação da v1.0).

4 itens são deliberadamente não-respondíveis pelo corpus (sondas de abstenção). As citações seguem o formato canônico (CTN, art. 173, I). Os doc_id do corpus são semânticos e estáveis (ctn:art-173) — métricas de retrieval são casamento determinístico de IDs, sem LLM.

Textos-fonte em domínio público (Lei 9.610/98, art. 8º, IV); camada de curadoria em CC-BY-4.0. Limitações: domínio único; 35 itens (desenhado para teste de regressão pareado); reflete a legislação compilada em julho/2026; ignora jurisprudência por construção; não é aconselhamento jurídico.