Datasets:
The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets
library). If this is not possible, please
open a discussion
for direct help.
Dataset Card for Corpus Carolina
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 v1.3, but v1.2 and v1.1 are 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-).
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 | 2076205 | 11 GB | |
dat | Datasets and other Corpora | 1074032 | 4.3 GB |
wik | Wikis | 957501 | 5.3 GB |
jud | Judicial Branch | 40398 | 1.5 GB |
leg | Legislative Branch | 13 | 25 MB |
soc | Social Media | 3294 | 17 MB |
uni | University Domains | 941 | 11 MB |
pub | Public Domain Works | 26 | 4.5 MB |
Data Fields
meta
: a XML string with a TEI conformantteiHeader
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}
}
- Downloads last month
- 211