---
YAML tags:

annotations_creators:
- expert-generated
language:
- es
language_creators:
- found
multilinguality:
- multilingual
pretty_name: MLDoc
license: cc-by-nc-4.0
size_categories: []
source_datasets: []
tags: []
task_categories:
- text-classification
task_ids: []

---


# MLDoc

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)


## Dataset Description
- **Website:** https://github.com/facebookresearch/MLDoc


### Dataset Summary

For document classification, we use the Multilingual Document Classification Corpus (MLDoc) [(Schwenk and Li, 2018; Lewis et al., 2004)](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf), a cross-lingual document classification dataset covering 8 languages. We use the Spanish portion to evaluate our models on monolingual classification. The corpus consists of 14,458 news articles from Reuters classified in four categories: Corporate/Industrial, Economics, Government/Social and Markets.

### Supported Tasks and Leaderboards

Text Classification

### Languages

The dataset is in English, German, French, Spanish, Italian, Russian, Japanese and Chinese. 

## Dataset Structure

### Data Instances

<pre>

</pre>

### Data Fields



### Data Splits

- esp.train: 273037 lines
- esp.testa: 54837 lines (used as dev)
- esp.testb: 53049 lines (used as test)

## Dataset Creation

### Curation Rationale
[N/A] 

### Source Data



#### Initial Data Collection and Normalization



#### Who are the source language producers?



### Annotations

#### Annotation process



#### Who are the annotators?



### Personal and Sensitive Information

[N/A]

## Considerations for Using the Data

### Social Impact of Dataset

This dataset contributes to the development of language models in Spanish.

### Discussion of Biases

[N/A]

### Other Known Limitations

[N/A]

## Additional Information

### Dataset Curators

[N/A]

### Licensing Information

Creative Commons Attribution Non Commercial 4.0

### Citation Information

The following paper must be cited when using this corpus:

```
@InProceedings{SCHWENK18.658,
  author = {Holger Schwenk and Xian Li},
  title = {A Corpus for Multilingual Document Classification in Eight Languages},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {may},
  date = {7-12},
  location = {Miyazaki, Japan},
  editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {979-10-95546-00-9},
  language = {english}
  }
  ```