tmquan/congbobanan-toaan-gov-vn

Công bố bản án — Vietnamese Court Judgments

Document-level mirror of the Vietnamese Cổng công bố bản án portal at congbobanan.toaan.gov.vn (Tòa án nhân dân tối cao — Supreme People's Court of Vietnam). Each case is provided as a raw PDF, parsed markdown, a structured JSON record (sidebar metadata + generic NER + statute references), a 2 048-dim dense embedding, and a 2-D projection (PCA / t-SNE / UMAP + HDBSCAN cluster id).

The corpus is produced end-to-end by the packages/datasites/congbobanan NeMo Curator pipeline:

download → parse → extract → embed → reduce

The corpus is a sibling of tmquan/anle-toaan-gov-vn: same pipeline, same shard convention, same field schema for the shared columns (doc_name, text, text_hash, embedding, ...); congbobanan adds the portal-specific sidebar columns (doc_type, ban_an_so, ngay, toa_an_xet_xu, loai_vu_viec, cap_xet_xu, quan_he_phap_luat, ...) and skips the án-lệ-only precedent fields. Consumers wanting both corpora can union the two on the shared subset of columns without any column-by-column reconciliation.

Quick start

The four configurations mirror the four pipeline stages 1-to-1; pick the one matching the granularity you need:

from datasets import load_dataset

# parse   — markdown body of every judgment under field `text`
parse   = load_dataset("tmquan/congbobanan-toaan-gov-vn", "parse",   split="train")
# extract — `text` + sidebar metadata + structured legal extraction (entities, statute refs)
extract = load_dataset("tmquan/congbobanan-toaan-gov-vn", "extract", split="train")
# embed   — 2 048-dim dense vectors
embed   = load_dataset("tmquan/congbobanan-toaan-gov-vn", "embed",   split="train")
# reduce  — pre-computed PCA / t-SNE / UMAP coordinates + HDBSCAN cluster id
reduce  = load_dataset("tmquan/congbobanan-toaan-gov-vn", "reduce",  split="train")

print(parse[0]["doc_name"], parse[0]["text"][:80])    # '1000023' '## Page 1\n\n…'
print(extract[0]["loai_vu_viec"], extract[0]["cap_xet_xu"])
print(len(embed[0]["embedding"]))                     # 2048
print(reduce[0])                                      # pca_x/y, tsne_x/y, umap_x/y, cluster_id

To download a slice of the raw artefacts:

from huggingface_hub import snapshot_download

# Just the parsed markdown
snapshot_download(
    repo_id="tmquan/congbobanan-toaan-gov-vn", repo_type="dataset",
    allow_patterns=["raw/md/*"], local_dir="congbobanan/md",
)

# Specific case ids
snapshot_download(
    repo_id="tmquan/congbobanan-toaan-gov-vn", repo_type="dataset",
    allow_patterns=[f"raw/pdf/{cid}.*" for cid in (1000023, 1000034)],
    local_dir="congbobanan/pdf",
)

Configurations

Each config below is a sharded parquet bundle with at most 10 000 rows per shard, named <stage>-<NNNNN>-of-<KKKKK>.parquet. Sharding is deterministic on doc_name (the zero-padded case_id) so re-publishes don't shuffle documents across shards. The convention matches tmquan/anle-toaan-gov-vn.

Config	Stage	Key columns
parse	parse	doc_name, case_id, source, detail_url, pdf_path, text, num_pages, char_len, confidence, parser_model, parsed_at, text_hash
extract	extract	doc_name, case_id, text_hash, text, entities, relations, statute_refs, doc_type, ban_an_so, ngay, ten_ban_an, ngay_cong_bo, quan_he_phap_luat, cap_xet_xu, loai_vu_viec, toa_an_xet_xu, ap_dung_an_le, dinh_chinh, thong_tin_vu_viec, tong_binh_chon, luot_xem, luot_tai, pdf_filename
embed	embed	doc_name, case_id, text_hash, embedding (2 048-d float), embedding_dim, embedding_model_id, embedding_chunks_used, embedding_chunking
reduce	reduce	doc_name, case_id, text_hash, pca_x/y, tsne_x/y, umap_x/y, cluster_id

text is the markdown body produced by the parse stage and copied verbatim into the extract stage. text_hash is a deterministic content hash that joins every config back to the per-doc shards under raw/.

Sidebar columns (extract config)

Promoted from the portal's right-side panel and parsed by CongbobananDocumentExtractor:

Field	Type	Description
doc_type	string	"ban-an" (Bản án / judgment) or "quyet-dinh" (Quyết định / decision).
ban_an_so	string	Case number, e.g. 03/2022/DSST.
ngay	string	Judgment date in site format (dd/mm/yyyy).
ten_ban_an	string	Human-readable case title.
ngay_cong_bo	string	Publication date (dd.mm.yyyy).
quan_he_phap_luat	string	Legal relationship / subject-matter label.
cap_xet_xu	string	Procedural level (Sơ thẩm / Phúc thẩm / Giám đốc thẩm / ...).
loai_vu_viec	string	Case type (Dân sự / Hình sự / Hành chính / ...).
toa_an_xet_xu	string	Issuing court name (full Vietnamese form).
ap_dung_an_le	string	Applied precedent, if any.
dinh_chinh	string	Corrections (đính chính).
thong_tin_vu_viec	string	Case info / summary blurb.
tong_binh_chon	string	Precedent-vote count (raw site string).
luot_xem	int64	View counter.
luot_tai	int64	Download counter.
pdf_filename	string	Original server-side PDF filename.

Repo layout

README.md                                     this dataset card
notebook.ipynb                                end-to-end EDA notebook (Plotly, LaTeX-style theme)
data/
  parse-<NNNNN>-of-<KKKKK>.parquet            `text` + parse metadata
  extract-<NNNNN>-of-<KKKKK>.parquet          `text` + sidebar + generic NER
  embed-<NNNNN>-of-<KKKKK>.parquet            2 048-d dense vectors
  reduce-<NNNNN>-of-<KKKKK>.parquet           PCA / t-SNE / UMAP + cluster id
assets/                                       static PNGs embedded in this README
raw/
  pdf/<case_id>.pdf                           original scraped PDF
  pdf/<case_id>.html                          cached detail HTML (iterator input)
  pdf/<case_id>.url                           source detail URL
  md/<case_id>.md                             parsed markdown body
  md/<case_id>.meta.json                      parser metadata sidecar
  jsonl/<task_id>.jsonl                       Extractor output (mirror of pipeline)

Pipeline summary

Stage	Reads	Writes	Tooling
download	integer IDs [start_id..end_id]	pdf/<case_id>.pdf + .html / .url	aiohttp scraper (CongbobananDocumentDownloader)
parse	pdf/*.pdf	md/<case_id>.md + <case_id>.meta.json	nvidia/nemoretriever-parse
extract	md/*.md	jsonl/<task_id>.jsonl	rule + LLM extractor (sidebar parser + generic NER)
embed	jsonl/*.jsonl	parquet/embeddings/<task_id>.parquet	nvidia/llama-nemotron-embed-1b-v2 (2 048-d, sliding window)
reduce	parquet/embeddings/*.parquet	parquet/reduced/<task_id>.parquet	scikit-learn PCA + t-SNE, umap-learn UMAP, HDBSCAN

Access caveat: VN egress required

congbobanan.toaan.gov.vn refuses TLS handshakes from non-Vietnamese source IPs with ERR_CONNECTION_CLOSED. Reproducing the download stage requires a Vietnamese VPS or a VN SOCKS5 / HTTPS proxy (cfg.scraper.proxy or HTTPS_PROXY). Reading the published parquet bundle from the Hub has no such restriction.

How to reproduce

# from the monorepo root
pip install -r packages/datasites/congbobanan/requirements.txt
python data/congbobanan.toaan.gov.vn/_to_hf.py \
    --repo tmquan/congbobanan-toaan-gov-vn

_to_hf.py does three things in order:

1. Consolidate every per-doc shard under parquet/embeddings/, parquet/reduced/ and jsonl/ into the four ZSTD parquet bundles in data/, each capped at 10 000 rows per shard and named <stage>-<NNNNN>-of-<KKKKK>.parquet. Rows are deterministically sorted on doc_name before sharding so re-runs produce byte-identical shard membership. DuckDB drives the embed / reduce consolidation because PyArrow ≥ 17 occasionally fails on the per-doc embedding shards with Repetition level histogram size mismatch.
2. Render the static plot PNGs in assets/ and the _stats.json snapshot embedded in this card (delegates to _render_assets.py).
3. Upload every artefact to the Hub at the right path. Importantly it uses HfApi.upload_folder(path_in_repo=...) — never hf upload-large-folder, which silently puts everything at the repo root because it has no --path-in-repo flag.

Sub-steps can be skipped with --skip-consolidate, --skip-assets, --no-upload. The heavy raw/pdf/ bucket can be skipped with --skip-raw-pdf during iteration (only the data/ parquets and the raw/jsonl + raw/md mirrors are needed for the load_dataset(...) path).

Source & license

The judgments are public legal documents published by the Supreme People's Court of Vietnam at https://congbobanan.toaan.gov.vn. Litigant identifiers in the source are already abbreviated by the publisher (e.g. Nguyễn Thị T). This redistribution is offered under CC-BY 4.0 with attribution to congbobanan.toaan.gov.vn. Users are responsible for complying with the original publisher's terms when reusing the raw PDFs.

Citation

If you use this dataset, please cite both the original portal and this redistribution:

@misc{congbobanan_toaan_2026,
  title        = {Vietnamese Court Judgments (congbobanan.toaan.gov.vn)},
  author       = {TMQuan},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/tmquan/congbobanan-toaan-gov-vn}},
  note         = {Document-level mirror of the Vietnamese court-judgment portal, with sidebar metadata, hierarchical structure layer, and 2 048-d dense embeddings.}
}

@misc{congbobanan_toaan_source_2026,
  title        = {Vietnamese Court Judgments},
  author       = {{Công bố bản án — Tòa án nhân dân tối cao}},
  year         = {2026},
  howpublished = {\url{https://congbobanan.toaan.gov.vn/}},
  note         = {Official portal for Vietnamese court judgments (bản án) + decisions (quyết định), published by the Supreme People's Court (Tòa án nhân dân tối cao).}
}