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- ---
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- annotations_creators:
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- - found
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- language_creators:
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- - found
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- language:
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- - en
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- license:
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- - unknown
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- multilinguality:
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- - monolingual
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- size_categories:
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- - 100K<n<1M
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- source_datasets:
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- - original
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- task_categories:
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- - text-classification
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- task_ids:
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- - topic-classification
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- paperswithcode_id: ag-news
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- pretty_name: AG’s News Corpus
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- dataset_info:
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- features:
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- - name: text
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- dtype: string
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- - name: label
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- dtype:
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- class_label:
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- names:
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- '0': World
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- '1': Sports
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- '2': Business
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- '3': Sci/Tech
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- splits:
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- - name: train
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- num_bytes: 29817303
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- num_examples: 120000
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- - name: test
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- num_bytes: 1879474
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- num_examples: 7600
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- download_size: 19820267
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- dataset_size: 31696777
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- configs:
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- - config_name: default
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- data_files:
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- - split: train
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- path: data/train-*
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- - split: test
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- path: data/test-*
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- train-eval-index:
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- - config: default
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- task: text-classification
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- task_id: multi_class_classification
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- splits:
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- train_split: train
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- eval_split: test
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- col_mapping:
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- text: text
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- label: target
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- metrics:
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- - type: accuracy
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- name: Accuracy
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- - type: f1
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- name: F1 macro
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- args:
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- average: macro
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- - type: f1
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- name: F1 micro
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- args:
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- average: micro
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- - type: f1
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- name: F1 weighted
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- args:
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- average: weighted
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- - type: precision
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- name: Precision macro
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- args:
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- average: macro
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- - type: precision
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- name: Precision micro
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- args:
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- average: micro
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- - type: precision
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- name: Precision weighted
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- args:
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- average: weighted
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- - type: recall
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- name: Recall macro
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- args:
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- average: macro
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- - type: recall
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- name: Recall micro
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- args:
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- average: micro
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- - type: recall
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- name: Recall weighted
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- args:
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- average: weighted
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- ---
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-
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- # Dataset Card for "ag_news"
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-
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- ## Table of Contents
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- - [Dataset Description](#dataset-description)
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- - [Dataset Summary](#dataset-summary)
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- - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- - [Languages](#languages)
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- - [Dataset Structure](#dataset-structure)
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- - [Data Instances](#data-instances)
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- - [Data Fields](#data-fields)
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- - [Data Splits](#data-splits)
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- - [Dataset Creation](#dataset-creation)
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- - [Curation Rationale](#curation-rationale)
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- - [Source Data](#source-data)
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- - [Annotations](#annotations)
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- - [Personal and Sensitive Information](#personal-and-sensitive-information)
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- - [Considerations for Using the Data](#considerations-for-using-the-data)
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- - [Social Impact of Dataset](#social-impact-of-dataset)
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- - [Discussion of Biases](#discussion-of-biases)
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- - [Other Known Limitations](#other-known-limitations)
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- - [Additional Information](#additional-information)
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- - [Dataset Curators](#dataset-curators)
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- - [Licensing Information](#licensing-information)
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- - [Citation Information](#citation-information)
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- - [Contributions](#contributions)
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-
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- ## Dataset Description
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-
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- - **Homepage:** [http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html](http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html)
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- - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- - **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- - **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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- - **Size of downloaded dataset files:** 31.33 MB
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- - **Size of the generated dataset:** 31.70 MB
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- - **Total amount of disk used:** 63.02 MB
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-
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- ### Dataset Summary
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-
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- AG is a collection of more than 1 million news articles. News articles have been
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- gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
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- activity. ComeToMyHead is an academic news search engine which has been running
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- since July, 2004. The dataset is provided by the academic comunity for research
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- purposes in data mining (clustering, classification, etc), information retrieval
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- (ranking, search, etc), xml, data compression, data streaming, and any other
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- non-commercial activity. For more information, please refer to the link
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- http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
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-
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- The AG's news topic classification dataset is constructed by Xiang Zhang
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- (xiang.zhang@nyu.edu) from the dataset above. It is used as a text
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- classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann
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- LeCun. Character-level Convolutional Networks for Text Classification. Advances
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- in Neural Information Processing Systems 28 (NIPS 2015).
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-
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- ### Supported Tasks and Leaderboards
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ### Languages
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ## Dataset Structure
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-
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- ### Data Instances
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-
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- #### default
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-
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- - **Size of downloaded dataset files:** 31.33 MB
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- - **Size of the generated dataset:** 31.70 MB
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- - **Total amount of disk used:** 63.02 MB
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-
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- An example of 'train' looks as follows.
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- ```
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- {
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- "label": 3,
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- "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..."
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- }
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- ```
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-
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- ### Data Fields
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-
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- The data fields are the same among all splits.
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-
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- #### default
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- - `text`: a `string` feature.
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- - `label`: a classification label, with possible values including `World` (0), `Sports` (1), `Business` (2), `Sci/Tech` (3).
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-
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- ### Data Splits
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-
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- | name |train |test|
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- |-------|-----:|---:|
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- |default|120000|7600|
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-
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- ## Dataset Creation
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-
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- ### Curation Rationale
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ### Source Data
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-
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- #### Initial Data Collection and Normalization
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- #### Who are the source language producers?
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ### Annotations
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-
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- #### Annotation process
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- #### Who are the annotators?
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ### Personal and Sensitive Information
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ## Considerations for Using the Data
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-
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- ### Social Impact of Dataset
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ### Discussion of Biases
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ### Other Known Limitations
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ## Additional Information
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-
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- ### Dataset Curators
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ### Licensing Information
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-
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- [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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-
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- ### Citation Information
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-
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- ```
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- @inproceedings{Zhang2015CharacterlevelCN,
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- title={Character-level Convolutional Networks for Text Classification},
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- author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
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- booktitle={NIPS},
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- year={2015}
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- }
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-
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- ```
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-
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-
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- ### Contributions
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-
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- Thanks to [@jxmorris12](https://github.com/jxmorris12), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@lewtun](https://github.com/lewtun) for adding this dataset.