Update README.md

README.md

@@ -1,24 +1,24 @@
 ---
 language:
 - ru
-- en
 license:
 - mit
 multilinguality:
-- monolingual
+- russian
 task_categories:
 - text-classification
 task_ids:
+- sentiment-classification
 - multi-class-classification
 - multi-label-classification
-pretty_name: RuGoEmotions
+pretty_name: RuIzardEmotions
 tags:
 - emotion
 size_categories:
 - 10K<n<100K
 ---
 
-# Dataset Card for GoEmotions
+# Dataset Card for 
 
 ## Table of Contents
 - [Dataset Description](#dataset-description)
@@ -46,8 +46,10 @@ size_categories:
 
 ### Dataset Summary
 
-The RuGoEmotions dataset contains 30k Reddit comments labeled for 10 emotion categories (joy, sadness, anger, enthusiasm, surprise, disgust, fear, guilt, shame and neutral).
-The dataset already with predefined train/val/test splits
+The RuIzardEmotions dataset is a high-quality translation of the [go-emotions](https://huggingface.co/datasets/go_emotions) dataset and the other [emotion-detection](https://www.kaggle.com/datasets/ishantjuyal/emotions-in-text/data) dataset. It contains 30k Reddit comments labeled for 10 emotion categories (__joy__, __sadness__, __anger__, __enthusiasm__, __surprise__, __disgust__, __fear__, __guilt__, __shame__ and __neutral__).
+The datasets were translated using the accurate translator DeepL and additional processing. The idea for the dataset was inspired by the [Izard's model](https://en.wikipedia.org/wiki/Differential_Emotions_Scale) of human emotions.
+
+The dataset already with predefined train/val/test splits.
 
 ### Supported Tasks and Leaderboards
 
@@ -63,48 +65,10 @@ The data is in Russian.
 
 Each instance is a reddit comment with one or more emotion annotations (or neutral).
 
-### Data Fields
-
-The configuration includes:
-- `text`: the reddit comment
-- `labels`: the emotion annotations
-
 ### Data Splits
 
 The simplified data includes a set of train/val/test splits with 24k, 3k, and 3k examples respectively.
 
-## Dataset Creation
-
-### Curation Rationale
-
-From the paper abstract:
-
-> Understanding emotion expressed in language has a wide range of applications, from building empathetic chatbots to
-detecting harmful online behavior. Advancement in this area can be improved using large-scale datasets with a
-fine-grained typology, adaptable to multiple downstream tasks.
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-Data was collected from Reddit comments via a variety of automated methods discussed in 3.1 of the paper.
-
-#### Who are the source language producers?
-
-English-speaking Reddit users.
-
-### Annotations
-
-#### Who are the annotators?
-
-Annotations were produced by 3 English-speaking crowdworkers in India.
-
-### Personal and Sensitive Information
-
-This dataset includes the original usernames of the Reddit users who posted each comment. Although Reddit usernames
-are typically disasociated from personal real-world identities, this is not always the case. It may therefore be
-possible to discover the identities of the individuals who created this content in some cases.
-
 ## Considerations for Using the Data
 
 ### Social Impact of Dataset
@@ -112,18 +76,7 @@ possible to discover the identities of the individuals who created this content
 Emotion detection is a worthwhile problem which can potentially lead to improvements such as better human/computer
 interaction. However, emotion detection algorithms (particularly in computer vision) have been abused in some cases
 to make erroneous inferences in human monitoring and assessment applications such as hiring decisions, insurance
-pricing, and student attentiveness (see
-[this article](https://www.unite.ai/ai-now-institute-warns-about-misuse-of-emotion-detection-software-and-other-ethical-issues/)).
-
-### Discussion of Biases
-
-From the authors' github page:
-
-> Potential biases in the data include: Inherent biases in Reddit and user base biases, the offensive/vulgar word lists used for data filtering, inherent or unconscious bias in assessment of offensive identity labels, annotators were all native English speakers from India. All these likely affect labelling, precision, and recall for a trained model. Anyone using this dataset should be aware of these limitations of the dataset.
-
-### Other Known Limitations
-
-[More Information Needed]
+pricing, and student attentiveness
 
 ## Additional Information
 
@@ -134,12 +87,14 @@ Researchers at Amazon Alexa, Google Research, and Stanford. See the [author list
 ### Licensing Information
 
 The GitHub repository which houses this dataset has an
-[Apache License 2.0](https://github.com/google-research/google-research/blob/master/LICENSE).
+[Apache License 2.0](https://github.com/Djacon/russian-emotion-detection/blob/main/LICENSE).
 
 ### Citation Information
 
+```
 @inproceedings{Djacon,
- author = {Djacon},
- title = {{RuGoEmotions: A Dataset of Fine-Grained Emotions}},
- year = {2023}
-}
+ author={Djacon},
+ title={RuIzardEmotions: A Dataset of Fine-Grained Emotions},
+ year={2023}
+}
+```