Upload README.md with huggingface_hub

README.md

@@ -3,4 +3,43 @@ tags:
 - sentiment-analysis
 language:
 - ind
----
+---
+
+The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
+publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
+Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii)
+15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline.
+Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated
+sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait.
+
+
+## Dataset Usage
+
+Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
+
+## Citation
+
+```@article{WILLIAM2020106231,
+title = "CLICK-ID: A novel dataset for Indonesian clickbait headlines",
+journal = "Data in Brief",
+volume = "32",
+pages = "106231",
+year = "2020",
+issn = "2352-3409",
+doi = "https://doi.org/10.1016/j.dib.2020.106231",
+url = "http://www.sciencedirect.com/science/article/pii/S2352340920311252",
+author = "Andika William and Yunita Sari",
+keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, Text-classification",
+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."
+}
+```
+
+## License
+
+Creative Commons Attribution 4.0 International
+
+## Homepage
+
+### NusaCatalogue
+
+For easy indexing and metadata: [https://indonlp.github.io/nusa-catalogue](https://indonlp.github.io/nusa-catalogue)