---
pretty_name: PT-BR Creative Corpus
language:
- pt
license: other
task_categories:
- text-generation
tags:
- pt-br
- brazilian-portuguese
- creative-writing
- literature
- pretraining
- continued-pretraining
configs:
- config_name: corpus
  default: true
  data_files:
  - split: corpus
    path: "data/corpus/*.parquet"
- config_name: documents
  data_files:
  - split: documents
    path: "data/documents/*.parquet"
- config_name: review_inventory
  data_files:
  - split: review
    path: "data/review_inventory/*.parquet"
- config_name: curation_lineage
  data_files:
  - split: lineage
    path: "data/curation_lineage/*.parquet"
- config_name: audit_metrics
  data_files:
  - split: audit
    path: "data/audit_metrics/*.parquet"
---

# PT-BR Creative Corpus v0.1.0

A curated Brazilian-Portuguese creative-writing corpus for continued pretraining / midtraining research.

## Status

This is the **canonical corpus freeze**, not a final model-specific training build.

- Canonical text units: **1,354**
- Document/edition entities: **803**
- Characters: **82,538,439**
- Words (whitespace count): **13,929,410**
- Historical project estimate: **18,339,188 chars/4.5 tokens**, retained only in the `audit_metrics` config.

The canonical `corpus` config deliberately contains **no generic token-count column**. Exact token counts belong to tokenizer-specific CPT builds.

## Load a small sample without loading the corpus

```python
from datasets import load_dataset

ds = load_dataset("oliveirabruno01/ptbr-creative-cpt", "corpus", split="corpus", streaming=True)
for row in ds.take(100):
    ...
```

## Load the full canonical corpus

```python
from datasets import load_dataset

ds = load_dataset("oliveirabruno01/ptbr-creative-cpt", "corpus", split="corpus")
```

For CPT, derive a model/run-specific training build from this corpus:

```text
canonical corpus
  -> work/author-level benchmark holdout + decontamination
  -> target model tokenizer
  -> EOS/document-boundary policy
  -> sequence packing for chosen context length
  -> trainer-specific binary/memmap format
```

Tokenized/packed sequences are intentionally **not** baked into this repository.

## Text-unit model

Most corpus rows are complete selected works/documents. FE-Unicamp is intentionally different: modern scholarly editions were segmented and only blocks classified as primary historical creative literature were retained. Those rows use `unit_kind="selected_segment"` and point to a real parent edition through `parent_document_id`.

For FE, `primary_authors` refers to the historical literary writer; modern scholarly editors are kept separately in `editors`.

## Orthography

`text_variant="original"` means the extracted source orthography is preserved. Any future AO90-normalized material must be released as a separate derived variant, never as a replacement for original text.

## Rights

Rights vary by source and are recorded per row with `rights_tier`, `license`, `license_url`, and `rights_evidence`.

Important caveat: the `ptbr-books-publicos` backbone is upstream-released under CC0-1.0 and described as public-domain, but this project did **not** independently establish per-item rights for every backbone book. The corpus therefore records that as a source-dataset claim rather than silently converting it into per-item legal provenance.

BBM scan-derived text is not included in `corpus`; it remains in the review/deferred inventory.

## Auxiliary configs

- `documents`: document/edition hierarchy.
- `review_inventory`: deferred and review queues, without training text.
- `curation_lineage`: acceptance/dedup/curation lineage for canonical units.
- `audit_metrics`: historical project accounting metrics. These are audit artifacts, not canonical token counts.

## Data architecture references

This repository keeps canonical text separate from tokenizer-specific training preprocessing, following the general architecture used by open pretraining projects such as Dolma/OLMo.

- Dolma data format: https://github.com/allenai/dolma/blob/main/docs/data-format.md
- OLMo memmap preprocessing: https://github.com/allenai/OLMo/blob/main/scripts/prepare_memmap_dataset.py
- Hugging Face manual dataset configs: https://huggingface.co/docs/hub/datasets-manual-configuration