celsowm/jurisprudencias_br
celsowm/jurisprudencias_br Brazilian jurisprudence (jurisprudência) collected by Juriscraper, published as Parquet for direct consumption by the Hugging Face Dataset Viewer, datasets, DuckDB, Polars, Pandas, and PyArrow. The default configuration is a Viewer-friendly projection with metadata and ementas. The complete payload is preserved in this same dataset under full/ for bulk downloads and analysis; it is intentionally not registered as a Viewer configuration because it is… See the full description on the dataset page: https://huggingface.co/datasets/celsowm/jurisprudencias_br.
celsowm/jurisprudencias_br
Brazilian jurisprudence (jurisprudência) collected by Juriscraper, published as Parquet for direct consumption by the Hugging Face Dataset Viewer, datasets, DuckDB, Polars, Pandas, and PyArrow.
The default configuration is a Viewer-friendly projection with metadata and ementas. The complete payload is preserved in this same dataset under full/ for bulk downloads and analysis; it is intentionally not registered as a Viewer configuration because it is too large for Dataset Server indexing.
Scope
Collected decisions from Brazilian courts. Supported courts (by tribunal value):
stfstjtjrj
Collection timestamp semantics
scraped_at: ISO-8601 UTC timestamp recorded when Juriscraper stored the row in its local operational SQLite database (collection time, not the court's publication time).data_julgamento/data_publicacao: normalizedYYYY-MM-DDstrings derived from the court payload when available; may be null.
Public schema
Default configuration
The default configuration intentionally omits the two largest payload columns (inteiro_teor and data_json) so the Dataset Viewer can browse the corpus. Use hf://datasets/celsowm/jurisprudencias_br/full/*.parquet when the complete payload is needed.
The local autoincrement id is not published. The stable logical document identity is the pair (tribunal, unique_key), which is also the deduplication key used by the scraper.
Default schema version: 2
Complete dataset
The Parquet files under full/ use the complete schema below. Their schema version is 1; their data_json column preserves the complete original source item so court-specific fields not in the flat columns remain available.
Process parties (polos)
partes is a UTF-8 JSON array of the process parties (polos do processo), one object per party:
[
{"nome": "MUNICÍPIO DE RIO DAS OSTRAS", "polo": "ativo", "tipo": "APELANTE"},
{"nome": "EDUARDO COIM MARTIN", "polo": "passivo", "tipo": "APELADO"}
]nome: party name (preserved with original capitalization/accents).polo: canonical side —ativo(plaintiff/appellant) orpassivo(defendant/appellee). Derived fromtipowhen the source exposes a label.tipo: the raw role label as the court presents it (e.g.APELANTE,APELADO,AGTE,AGDO,AUTOR,RÉU,RECORRENTE,RECLAMANTE...). Sources without structured parties (e.g. TJRJ) are enriched from the process detail page; some courts may leavepartesnull.
Source-specific variability
Different courts return different payloads. Fields outside the flat schema are embedded verbatim in data_json (UTF-8 JSON). Always read data_json when you need fields not listed above.
Licensing / provenance
Decisions are public-domain government works in Brazil, but verify before any commercial use. Provenance is the respective court website; Juriscraper is a non-official collector.
Update cadence
Published as full deterministic snapshots from the authoritative local SQLite database. See the project's docs/HUGGINGFACE_DATASET_PUBLISHING_REFACTOR_PLAN.md.
Usage
With datasets
from datasets import load_dataset
ds = load_dataset("celsowm/jurisprudencias_br")With DuckDB / SQL
SELECT tribunal, count(*)
FROM 'hf://datasets/celsowm/jurisprudencias_br/data/*.parquet'
GROUP BY tribunal;With PyArrow / Pandas
import pyarrow.dataset as ds
dataset = ds.dataset(
f"hf://datasets/celsowm/jurisprudencias_br/data/",
format="parquet",
)Snapshot metadata
- Snapshot boundary (max local id):
2059895 - Published rows:
781481 - Shards:
16
Legacy raw snapshot
If present, jurisprudencia.db is a raw operational snapshot of the SQLite storage engine used during scraping. It is not the preferred ML format; prefer the Parquet files under data/ for analysis and training.
