The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600. Version 3, Updated 09/09/2015
Usage
dataset_ag_news(
dir = NULL,
split = c("train", "test"),
delete = FALSE,
return_path = FALSE,
clean = FALSE,
manual_download = FALSE
)
Source
http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html
https://github.com/srhrshr/torchDatasets/raw/master/dbpedia_csv.tar.gz
Arguments
- dir
Character, path to directory where data will be stored. If
NULL
, user_cache_dir will be used to determine path.- split
Character. Return training ("train") data or testing ("test") data. Defaults to "train".
- delete
Logical, set
TRUE
to delete dataset.- return_path
Logical, set
TRUE
to return the path of the dataset.- clean
Logical, set
TRUE
to remove intermediate files. This can greatly reduce the size. Defaults to FALSE.- manual_download
Logical, set
TRUE
if you have manually downloaded the file and placed it in the folder designated by running this function withreturn_path = TRUE
.
Value
A tibble with 120,000 or 30,000 rows for "train" and "test" respectively and 3 variables:
- class
Character, denoting new class
- title
Character, title of article
- description
Character, description of article
See also
Other topic:
dataset_dbpedia()
,
dataset_trec()
Examples
if (FALSE) {
dataset_ag_news()
# Custom directory
dataset_ag_news(dir = "data/")
# Deleting dataset
dataset_ag_news(delete = TRUE)
# Returning filepath of data
dataset_ag_news(return_path = TRUE)
# Access both training and testing dataset
train <- dataset_ag_news(split = "train")
test <- dataset_ag_news(split = "test")
}