Update README.md

README.md

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----
-license: openrail++
----
+---
+license: openrail++
+task_categories:
+- text-classification
+language:
+- uk
+tags:
+- emotion
+pretty_name: emobench-ua
+size_categories:
+- 1K<n<10K
+---
+
+# EmoBench-UA: Emotions Detection Dataset in Ukrainian Texts
+
+<img alt="EmoBench-UA" src="intro_logo.png">
+
+**EmoBench-UA**: the first of its kind emotions detection dataset in Ukrainian texts. This dataset covers the detection of basic emotions: Joy, Anger, Fear, Disgust, Surprise, Sadness, or None.
+Any text can contain any amount of emotion -- only one, several, or none at all. The texts with *None* emotions are the ones where the labels per emotions classes are 0.
+
+*Binary*: specifically this dataset contains binary labels indicating simply presence of any emotion in the text.
+
+## Data Collection
+The data collection was done via [Toloka.ai](https://toloka.ai}{https://toloka.ai) crowdsourcing platform. As per original Ukrainian texts, we used the opensourced [corpus of Ukrainian tweets](https://github.com/kateryna-bobrovnyk/ukr-twi-corpus).
+
+## Splits Statistics
+
+Overall, the datasets contains of 4949 labelled instances. Krippendorff's alpha annotation agreement score is 0.85. 
+Then, we partitioned the dataset into fixed train/development/test subsets following a 50/5/45% split ratio.
+
+<img alt="Splits Statistic" src="data_stats.png">
+
+## Citation
+
+If you would like to acknowledge our work, please, cite the following manuscript:
+
+```
+TDB
+```
+
+## Contacts
+
+[Daryna Dementieva](https://huggingface.co/dardem), [Nikolay Babakov](https://huggingface.co/NiGuLa)