{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "baruquerodrigues-llm-legislativo--reuse-curated-legislative-corpus",
 "repo": "BaruqueRodrigues/llm-legislativo",
 "repo_url": "https://github.com/BaruqueRodrigues/llm-legislativo",
 "repo_slug": "baruquerodrigues-llm-legislativo",
 "title": "Build a Portuguese legislative text dataset",
 "page_title": "How to build a Portuguese legislative text dataset with LLM Legislativo",
 "summary": "Load the curated Parquet release with leakage-free train, validation, and test splits for NLP work.",
 "who": "A data engineer or NLP researcher who needs Brazilian legislative text with splits that do not leak related documents across sets.",
 "problem": "Legislative documents are amended and reported on in families, so random splits leak near-duplicate text into the test set.",
 "outcome": "A train, validation, and test dataset built from the published Parquet files, split by family_id.",
 "steps": [
  "Open https://huggingface.co/datasets/BaruqRodrigues/corpus-legislativo-pretraining and read the split and schema description.",
  "Install the Hugging Face datasets library with `pip install datasets`. (inferred, check the README)",
  "Load the release with `from datasets import load_dataset` and call it on the Hub repo BaruqRodrigues/corpus-legislativo-pretraining. (inferred, check the README)",
  "Check that the test split has no family_id that appears in the train split before using the data."
 ],
 "agent_prompt": "Load the Hugging Face dataset BaruqRodrigues/corpus-legislativo-pretraining and report the number of rows in the train, validation, and test splits. Output a table of counts and one sample record per split. Verify that no family_id appears in both train and test.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "45 minutes",
 "basis": "readme",
 "caveat": "The README gives the split ratios and the family_id method, but the exact column names are not in the README, so check the dataset card before writing code.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "A Python environment (the README install section is cut off in the source, so exact setup steps are unconfirmed)",
  "Internet access to Hugging Face Hub and GitHub",
  "Enough disk space for the Parquet release if you download corpus files (the raw TAR archives total over 360 GiB)",
  "Basic familiarity with PyTorch and Hugging Face datasets for the model and corpus steps"
 ],
 "risk": "dual-use",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": false,
 "score": 0.8,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/baruquerodrigues-llm-legislativo/reuse-curated-legislative-corpus/",
 "md": "https://dedupedai.com/use-cases/baruquerodrigues-llm-legislativo/reuse-curated-legislative-corpus/index.md",
 "disclaimer": "Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked \"Seen in the wild\", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/baruquerodrigues-llm-legislativo--reuse-curated-legislative-corpus.json",
 "api_version": "v1"
}