metadata

annotations_creators:
  - no-annotation
language_creators:
  - crowdsourced
language:
  - pt
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 1B<n<10B
source_datasets:
  - original
task_categories:
  - fill-mask
  - text-generation
task_ids:
  - masked-language-modeling
  - language-modeling
pretty_name: Carolina
language_bcp47:
  - pt-BR

Dataset Card for Corpus Carolina

Table of Contents
Dataset Description
Dataset Structure
Additional Information

Dataset Description

Homepage: sites.usp.br/corpuscarolina
Current Version: 1.2 (Ada)
Point of Contact: LaViHD

Dataset Summary

Carolina is an Open Corpus for Linguistics and Artificial Intelligence with a robust volume of texts of varied typology in contemporary Brazilian Portuguese (1970-). This corpus contains documents and texts extracted from the web and includes information (metadata) about its provenance and tipology.

The documents are clustered into taxonomies and the corpus can be loaded in complete or taxonomy modes. To load a single taxonomy, it is possible to pass a code as a parameter to the loading script (see the example bellow). Codes are 3-letters string and possible values are:

dat : datasets and other corpora;
jud : judicial branch;
leg : legislative branch;
pub : public domain works;
soc : social media;
uni : university domains;
wik : wikis.

Dataset Vesioning:

The Carolina Corpus is under continuous development resulting in multiple vesions. The current version is v1.2, but v1.1 is also available. You can access diferent vesions of the corpus using the revision parameter on load_dataset.

Usage Example:

from datasets import load_dataset

# to load all taxonomies
corpus_carolina = load_dataset("carolina-c4ai/corpus-carolina")

# to load social media documents
social_media = load_dataset("carolina-c4ai/corpus-carolina", taxonomy="soc")

# to load previous version
corpus_carolina = load_dataset("carolina-c4ai/corpus-carolina", revision="v1.1")

Supported Tasks

Carolina corpus was compiled for academic purposes, namely linguistic and computational analysis.

Languages

Contemporary Brazilian Portuguese (1970-2021).

Dataset Structure

Files are stored inside corpus folder with a subfolder for each taxonomy. Every file folows a XML structure (TEI P5) and contains multiple extracted documents. For each document, the text and metadata are exposed as text and meta features, respectively.

Data Instances

Every instance have the following structure.

{
    "meta": datasets.Value("string"),
    "text": datasets.Value("string")
}

Code	Taxonomy	Instances	Size
	Total	2107045	11 GB
dat	Datasets and other Corpora	1102049	4.4 GB
wik	Wikis	960139	5.2 GB
jud	Judicial Branch	40464	1.5 GB
leg	Legislative Branch	13	25 MB
soc	Social Media	3413	17 MB
uni	University Domains	941	10 MB
pub	Public Domain Works	26	4.5 MB

Data Fields

meta: a XML string with a TEI conformant teiHeader tag. It is exposed as text and needs to be parsed in order to access the actual metada;
text: a string containing the extracted document.

Data Splits

As a general corpus, Carolina does not have splits. In order to load the dataset, it is used corpus as its single split.

Additional Information

Dataset Curators

The Corpus Carolina is developed by a multidisciplinary team of linguists and computer scientists, members of the Virtual Laboratory of Digital Humanities - LaViHD and the Artificial Intelligence Center of the University of São Paulo - C4AI.

Licensing Information

The Open Corpus for Linguistics and Artificial Intelligence (Carolina) was compiled for academic purposes, namely linguistic and computational analysis. It is composed of texts assembled in various digital repositories, whose licenses are multiple and therefore should be observed when making use of the corpus. The Carolina headers are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International."

Citation Information

@misc{crespo2023carolina,
      title={Carolina: a General Corpus of Contemporary Brazilian Portuguese with Provenance, Typology and Versioning Information}, 
      author={Maria Clara Ramos Morales Crespo and Maria Lina de Souza Jeannine Rocha and Mariana Lourenço Sturzeneker and Felipe Ribas Serras and Guilherme Lamartine de Mello and Aline Silva Costa and Mayara Feliciano Palma and Renata Morais Mesquita and Raquel de Paula Guets and Mariana Marques da Silva and Marcelo Finger and Maria Clara Paixão de Sousa and Cristiane Namiuti and Vanessa Martins do Monte},
      year={2023},
      eprint={2303.16098},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}