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Datasets:
dataseek
/
ptbr-blogs

Tasks:
Text Generation
Modalities:
Tabular
Text
Languages:
Portuguese
Size:
100K - 1M
Tags:
pt-br
brazilian-portuguese
blogs
long-form
c4
pretraining
License:
Dataset card Data Studio Files
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ptbr-blogs / README.md
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Initial dataset release — MagTina350m pretrain corpus slice
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metadata 
license: odc-by
language:
  - pt
pretty_name: PT-BR Blogs (long-form, C4-derived)
size_categories:
  - 100K<n<1M
task_categories:
  - text-generation
tags:
  - pt-br
  - brazilian-portuguese
  - blogs
  - long-form
  - c4
  - pretraining
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/*.parquet

PT-BR Blogs (long-form, C4-derived)

Part of the MagTina350m pretrain corpus release by Dataseek under the Magestic.ai brand. This is one of nine silver-layer datasets that fed dataseek/magtina350m-base.

Summary

185 K long-form Brazilian-Portuguese blog posts (≥ 5 K words each) extracted from C4 by filtering Blogspot, WordPress, Medium and similar platform domains. Higher per-document quality than generic web; useful for stylistic diversity and long-context training.

Source and collection method

Source: AllenAI C4 PT-BR slice → blog-platform domain filter → minimum-length gate (≥ 5 000 words) → NFKD normalisation → URL dedup.

ETL script (in the MagTina1B repository): scripts/etl/14_blogs_c4_v1.py (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:

  • Blog-platform domains only (blogspot, wordpress, medium, ghost, …)
  • Word count ≥ 5 000 (long-form gate)
  • URL dedup across web corpora

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 int64 Stable row id.
text string Blog post body.
url string Source URL.
domain string Hosting platform domain.
timestamp timestamp Crawl timestamp.
n_chars int32 Character count of text.

Columns dropped at export (kept private as ETL internals): quality_score

Size statistics

Metric Value
Rows 185.0 K (185,012)
Characters 6.92 B (6,923,317,314)
Estimated tokens (PT-BR, chars / 4.5) 1.54 B
Compressed Parquet on disk ~3.87 GB

Used in MagTina350m pretrain: 1.540 B tokens (8.9 % of MagTina350m's 17.39 B-token pretrain budget).

How to load 

from datasets import load_dataset

ds = load_dataset("dataseek/ptbr-blogs", 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

ODC-BY 1.0 (CommonCrawl-derived). Long-form blog content; individual posts remain copyright of their authors. Use for non-commercial research is the safest interpretation; commercial use requires per-author clearance.

Upstream attribution: AllenAI C4 + individual blog authors

Citation

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

@misc{magtina350m_pretrain_2026,
  title  = {MagTina350m pretrain corpus — PT-BR Blogs (long-form, C4-derived)},
  author = {Frasson, Ricardo and {Dataseek Team}},
  year   = 2026,
  publisher = {Hugging Face},
  url    = {https://huggingface.co/datasets/dataseek/ptbr-blogs}
}

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

License

odc-by