Authorization notice. LLM Legislativo is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.
Who it is forA data engineer or NLP researcher who needs Brazilian legislative text with splits that do not leak related documents across sets.
The problemLegislative documents are amended and reported on in families, so random splits leak near-duplicate text into the test set.
The outcomeA train, validation, and test dataset built from the published Parquet files, split by family_id.
What you need first
- 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
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_datasetand 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.
Prompt for your agent
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.
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.
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