Dataset:
ag_news
0

Task Categories: text-classification
Languages: en
Multilinguality: monolingual
Size Categories: 100K<n<1M
Licenses: unknown
Language Creators: found
Annotations Creators: found
Source Datasets: original

Dataset Card for "ag_news"

Dataset Summary

AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .

The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances

default

  • Size of downloaded dataset files: 29.88 MB
  • Size of the generated dataset: 30.23 MB
  • Total amount of disk used: 60.10 MB

An example of 'train' looks as follows.

{
    "label": 3,
    "text": "New iPad released Just like every other September, this one is no different. Apple is planning to release a bigger, heavier, fatter iPad that..."
}

Data Fields

The data fields are the same among all splits.

default

  • text: a string feature.
  • label: a classification label, with possible values including World (0), Sports (1), Business (2), Sci/Tech (3).

Data Splits

name train test
default 120000 7600

Dataset Creation

Curation Rationale

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

@inproceedings{Zhang2015CharacterlevelCN,
  title={Character-level Convolutional Networks for Text Classification},
  author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
  booktitle={NIPS},
  year={2015}
}

Contributions

Thanks to @jxmorris12, @thomwolf, @lhoestq, @lewtun for adding this dataset.

Models trained or fine-tuned on ag_news