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Update files from the datasets library (from 1.3.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.3.0

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  1. README.md +5 -0
README.md CHANGED
@@ -44,6 +44,7 @@ task_ids:
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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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  ## Dataset Description
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@@ -171,3 +172,7 @@ keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, T
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  abstract = "News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas."
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  }
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  ```
 
 
 
 
 
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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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  ## Dataset Description
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  abstract = "News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas."
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  }
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  ```
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+ ### Contributions
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+ Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.