Initial dataset release — MagTina350m pretrain corpus slice

README.md

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+---
+license: cc-by-4.0
+language:
+- pt
+pretty_name: PT-BR Academic Corpus (CAPES theses + SciELO abstracts/books)
+size_categories:
+- 1M<n<10M
+task_categories:
+- text-generation
+tags:
+- pt-br
+- brazilian-portuguese
+- academic
+- theses
+- abstracts
+- pretraining
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/*.parquet
+---
+
+# PT-BR Academic Corpus (CAPES theses + SciELO abstracts/books)
+
+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
+
+1.86 M Brazilian-Portuguese academic documents combining CAPES thesis and dissertation abstracts, SciELO article abstracts and SciELO open-access books. Complements the fulltext SciELO articles corpus with broader coverage at shorter document lengths.
+
+## Source and collection method
+
+Sources: chenghao/scielo_books, eduagarcia/scielo_abstracts, eduagarcia/capes_teses_dissertacoes (all HuggingFace) → NFKD → SHA-1(text[:512]) dedup → per-row `source` tag preserved.
+
+**ETL script (in the MagTina1B repository):** [`scripts/etl/17_academic_pt_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:
+
+- alpha_ratio ≥ 0.65
+- len(text) ≥ 200 chars
+- SHA-1 dedup across all three upstream sources
+
+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 |
+|---|---|---|
+| `source` | `string` | Upstream identifier. |
+| `text` | `string` | Document text. |
+| `n_chars` | `int32` | Character count. |
+| `n_words` | `int32` | Word count. |
+| `meta_json` | `string` | JSON-encoded source-specific metadata. |
+
+Columns dropped at export (kept private as ETL internals): *none*
+
+## Size statistics
+
+| Metric | Value |
+|---|---:|
+| Rows | 1.86 M (1,862,793) |
+| Characters | 4.45 B (4,449,658,548) |
+| Estimated tokens (PT-BR, chars / 4.5) | 988.81 M |
+| Compressed Parquet on disk | ~2.49 GB |
+
+**Used in MagTina350m pretrain:** 0.989 B tokens
+(5.7 % of MagTina350m's 17.39 B-token pretrain budget).
+
+## How to load
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("dataseek/ptbr-academic", 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
+
+Mixed CC-BY 4.0 (SciELO subset) and CC-BY-SA / institutional-academic licensing for CAPES theses & dissertations. The conservative interpretation is to treat the aggregate as CC-BY 4.0 with attribution to the per-row `source`. See `meta_json` for upstream identifiers.
+
+**Upstream attribution:** chenghao/scielo_books, eduagarcia/scielo_abstracts, eduagarcia/capes_teses_dissertacoes (HuggingFace)
+
+## Citation
+
+If you use this dataset, please cite both the upstream source and MagTina350m:
+
+```bibtex
+@misc{magtina350m_pretrain_2026,
+  title  = {MagTina350m pretrain corpus — PT-BR Academic Corpus (CAPES theses + SciELO abstracts/books)},
+  author = {Frasson, Ricardo and {Dataseek Team}},
+  year   = 2026,
+  publisher = {Hugging Face},
+  url    = {https://huggingface.co/datasets/dataseek/ptbr-academic}
+}
+```
+
+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
+
+[cc-by-4.0](https://spdx.org/licenses/cc-by-4.0.html)

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