Datasets:
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- es-VE
license:
- cc-by-nc-nd-4.0
multilinguality:
- monolingual
pretty_name: mammut-corpus-venezuela
size_categories:
- unknown
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
mammut-corpus-venezuela
HuggingFace Dataset for testing purposes. The train dataset is mammut/mammut-corpus-venezuela.
1. How to use
How to load this dataset directly with the datasets library:
>>> from datasets import load_dataset>>> dataset = load_dataset("mammut/mammut-corpus-venezuela")
2. Dataset Summary
mammut-corpus-venezuela is a dataset for Spanish language modeling. This dataset comprises a large number of Venezuelan and Latin-American Spanish texts, manually selected and collected in 2021. The data was collected by a process of web scraping from different portals, downloading of Telegram group chats' history, and selecting of Venezuelan and Latin-American Spanish corpus available online. The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers. Social biases may be present, and a percentage of the texts may be fake or contain misleading or offensive language.
Each record in the dataset contains the author of the text (anonymized for conversation authors), the date on which the text entered in the corpus, the text which was automatically tokenized at sentence level for sources other than conversations, the source of the text, the title of the text, the number of tokens (excluding punctuation marks) of the text, and the linguistic register of the text.
This is the test set for mammut/mammut-corpus-venezuela dataset.
3. Supported Tasks and Leaderboards
This dataset can be used for language modeling testing.
4. Languages
The dataset contains Venezuelan and Latin-American Spanish.
5. Dataset Structure
Dataset structure features.
5.1 Data Instances
An example from the dataset:
"AUTHOR":"author in title",
"TITLE":"Luis Alberto Buttó: Hecho en socialismo",
"SENTENCE":"Históricamente, siempre fue así.",
"DATE":"2021-07-04 07:18:46.918253",
"SOURCE":"la patilla",
"TOKENS":"4",
"TYPE":"opinion/news",
The average word token count are provided below:
5.2 Total of tokens (no spelling marks)
Test: 4,876,739.
5.3 Data Fields
The data have several fields:
AUTHOR: author of the text. It is anonymized for conversation authors.
DATE: date on which the text was entered in the corpus.
SENTENCE: text. It was automatically tokenized for sources other than conversations.
SOURCE: source of the texts.
TITLE: title of the text from which SENTENCE originates.
TOKENS: number of tokens (excluding punctuation marks) of SENTENCE.
TYPE: linguistic register of the text.
5.4 Data Splits
The mammut-corpus-venezuela dataset has 2 splits: train and test. Below are the statistics:
Number of Instances in Split.
Test: 157,011.
6. Dataset Creation
6.1 Curation Rationale
The purpose of the mammut-corpus-venezuela dataset is language modeling. It can be used for pre-training a model from scratch or for fine-tuning on another pre-trained model.
6.2 Source Data
6.2.1 Initial Data Collection and Normalization
The data consists of opinion articles and text messages. It was collected by a process of web scraping from different portals, downloading of Telegram group chats’ history and selecting of Venezuelan and Latin-American Spanish corpus available online.
The text from the web scraping process was separated in sentences and was automatically tokenized for sources other than conversations.
An arrow parquet file was created.
Text sources: El Estímulo (website), cinco8 (website), csm-1990 (oral speaking corpus), "El atajo más largo" (blog), El Pitazo (website), La Patilla (website), Venezuelan movies subtitles, Preseea Mérida (oral speaking corpus), Prodavinci (website), Runrunes (website), and Telegram group chats.
6.2.2 Who are the source language producers?
The texts come from Venezuelan Spanish speakers, subtitlers, journalists, politicians, doctors, writers, and online sellers.
6.3 Annotations
6.3.1 Annotation process
At the moment the dataset does not contain any additional annotations.
6.3.2 Who are the annotators?
Not applicable.
6.4 Personal and Sensitive Information
The data is partially anonymized. Also, there are messages from Telegram selling chats, some percentage of these messages may be fake or contain misleading or offensive language.
7. Considerations for Using the Data
7.1 Social Impact of Dataset
The purpose of this dataset is to help the development of language modeling models (pre-training or fine-tuning) in Venezuelan Spanish.
7.2 Discussion of Biases
Most of the content comes from political, economical and sociological opinion articles. Social biases may be present.
7.3 Other Known Limitations
(If applicable, description of the other limitations in the data.)
Not applicable.
8. Additional Information
8.1 Dataset Curators
The data was originally collected by Lino Urdaneta and Miguel Riveros from Mammut.io.
8.2 Licensing Information
Not applicable.
8.3 Citation Information
Not applicable.
8.4 Contributions
Not applicable.