README.md

---
license: cc0-1.0
language:
- pt
pretty_name: Diário Oficial da União 2025-2026 (DO1 + DO1E)
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- pt-br
- brazilian-portuguese
- government
- legal
- dou
- diario-oficial
- pretraining
configs:
- config_name: default
  data_files:
  - split: train
    path: data/*.parquet
---

# Diário Oficial da União 2025-2026 (DO1 + DO1E)

Part of the **MagTina350m pretrain corpus release** by [Dataseek](https://dataseek.com.br)
under the Magestic.ai brand. This is one of nine silver-layer datasets that fed
[`dataseek/magtina350m-base`](https://huggingface.co/dataseek/magtina350m-base).

## Summary

20 K articles from the Brazilian Official Federal Gazette covering 2025-2026 — sections DO1 (regular edition) and DO1E (extra edition). Rich structured metadata: publication date, edition, page, PDF URL, ementa (summary), full text. Small corpus but high signal for legal/admin PT-BR.

## Source and collection method

Source: INLABS portal — https://inlabs.in.gov.br/ → daily ZIP downloads → XML parse → DO1 + DO1E section filter → dedup by `id_materia`.

**ETL script (in the MagTina1B repository):** [`scripts/etl/09_dou_2026_v1.py`](https://huggingface.co/dataseek/magtina350m-base) *(public release of the ETL scripts is on the roadmap; until then the data card below documents the recipe in full).*

## Filters and deduplication

The following filters were applied before this dataset reached its silver
(release-ready) state:

- Sections DO1 (regular) and DO1E (extra) only
- Dedup by `id_materia`

Global URL-normalised deduplication was applied across all web-derived corpora
(`webpages`, `news`, `blogs`) so the same article does not appear twice across
those three datasets.

## Schema

| Column | Type | Description |
|---|---|---|
| `id_materia` | `string` | INLABS materia identifier. |
| `article_id` | `string` | Per-article id within materia. |
| `pub_name` | `string` | Publication identifier (e.g. 'DO1'). |
| `pub_date` | `timestamp` | Publication date. |
| `art_type` | `string` | Article type. |
| `art_category` | `string` | Article category. |
| `art_class` | `string` | Article subclass. |
| `edition_number` | `string` | Edition number. |
| `page_number` | `string` | Page in the printed edition. |
| `pdf_url` | `string` | URL to the official PDF. |
| `identifica` | `string` | Short identifier line (e.g. ato number). |
| `titulo` | `string` | Article title. |
| `ementa` | `string` | Official summary (when present). |
| `text` | `string` | Full article body. |
| `n_chars` | `int32` | Character count of `text`. |
| `n_words` | `int32` | Word count of `text`. |

Columns dropped at export (kept private as ETL internals): *none*

## Size statistics

| Metric | Value |
|---|---:|
| Rows | 20.1 K (20,124) |
| Characters | 75.63 M (75,633,413) |
| Estimated tokens (PT-BR, chars / 4.5) | 16.81 M |
| Compressed Parquet on disk | ~0.04 GB |

**Used in MagTina350m pretrain:** 0.017 B tokens
(0.1 % of MagTina350m's 17.39 B-token pretrain budget).

## How to load

```python
from datasets import load_dataset

ds = load_dataset("dataseek/ptbr-dou", split="train", streaming=True)
for row in ds.take(5):
    print(row["text"][:200])
```

Streaming is recommended for the larger configs. For the smaller datasets
(`ptbr-dou`, `ptbr-books-publicos`) eager loading is fine.

## Licensing

CC0 1.0 / Public Domain. Official acts of the Brazilian federal government are not protected by copyright (Lei 9.610/98 art. 8 § I). Freely redistributable.

**Upstream attribution:** INLABS / Imprensa Nacional — https://inlabs.in.gov.br/ (Brazilian Federal Press Office)

## Citation

If you use this dataset, please cite both the upstream source and MagTina350m:

```bibtex
@misc{magtina350m_pretrain_2026,
  title  = {MagTina350m pretrain corpus — Diário Oficial da União 2025-2026 (DO1 + DO1E)},
  author = {Frasson, Ricardo and {Dataseek Team}},
  year   = 2026,
  publisher = {Hugging Face},
  url    = {https://huggingface.co/datasets/dataseek/ptbr-dou}
}
```

Please also honour the upstream license terms — for CC-BY-derived data,
attribution to the upstream creators is mandatory; for CC-BY-SA, downstream
derivatives must remain CC-BY-SA-compatible.

## Intended use

- Pre-training, continued pre-training, or domain-adapting of Brazilian Portuguese
  language models.
- PT-BR NLP research where statistically representative public-web / academic /
  legal / encyclopedic data is needed.
- Reproducing or improving on the MagTina350m result.

## Known limitations and PII statement

- **Text was NOT PII-scrubbed.** URLs, emails, phone numbers and personal names
  that occurred in the source data may still be present. We strip zero-width
  characters and normalise Unicode but we do not run an NER pass.
- **Crawled data carries upstream biases** of CommonCrawl, Wikipedia, news outlets
  and academic institutions present in the source. We have not audited these.
- **No safety filtering** beyond langid and basic alpha-ratio gates. Hate-speech,
  spam and adult content present in the source remain unless caught incidentally.
- **Provenance preserved at row level.** Every row has either a `url`, `source` or
  `doc_id` column that points back to upstream — this is intentional, so consumers
  can re-license, redact or filter.

## Related releases

- **Model:** [`dataseek/magtina350m-base`](https://huggingface.co/dataseek/magtina350m-base) (354.6 M params, pretrained on this corpus + 8 sibling datasets)
- **Instruct model:** [`dataseek/magtina350m-instruct`](https://huggingface.co/dataseek/magtina350m-instruct)
- **Sibling datasets:** see `dataseek/ptbr-*` for all nine corpora

## License

[cc0-1.0](https://spdx.org/licenses/cc0-1.0.html)