
## Short Description

The __rubert-tiny2-russian-emotion-detection__ is a fine-tuned [rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model for multi-label __emotion classification__ task, specifically on Russian texts. Trained on custom [ru-izard-emotions](https://huggingface.co/datasets/Djacon/ru-izard-emotions) dataset, so this model can recognize a spectrum of 9 emotions, including __joy__, __sadness__, __anger__, __enthusiasm__, __surprise__, __disgust__, __fear__, __guilt__, __shame__ + __neutral__ (no emotion). Project was inspired by the [Izard's model](https://en.wikipedia.org/wiki/Differential_Emotions_Scale) of human emotions.

For more information about model, please check [Github repository](https://github.com/Djacon/russian-emotion-detection)

## Training Parameters:
```yaml
Optimizer: AdamW
Schedule: LambdaLR
Learning Rate: 1e-4
Batch Size: 64
Number Of Epochs: 10
```

## Emotion Categories:
```js
0. Neutral (Нейтрально)
1. Joy (Радость)
2. Sadness (Грусть)
3. Anger (Гнев)
4. Enthusiasm (Интерес)
5. Surprise (Удивление)
6. Disgust (Отвращение)
7. Fear (Страх)
8. Guilt (Вина)
9. Shame (Стыд)
```

## Test results:

||Neutral|Joy|Sadness|Anger|Enthusiasm|Surprise|Disgust|Fear|Guilt|Shame|Mean|
|-|-|-|-|-|-|-|-|-|-|-|-|
|AUC|0.7319|0.8234|0.8069|0.7884|0.8493|0.8047|0.8147|0.9034|0.8528|0.7145|0.8090|
|F1 micro|0.7192|0.7951|0.8204|0.7642|0.8630|0.9032|0.9156|0.9482|0.9526|0.9606|0.8642|
|F1 macro|0.6021|0.7237|0.6548|0.6274|0.7291|0.5712|0.4780|0.8158|0.4879|0.4900|0.6180|


## Citations
```
@misc{Djacon,
  author={Djacon},
  year={2023},
  publisher={Hugging Face},
  journal={Hugging Face Hub},
}
```
{"language": ["ru", "en"], "license": "mit", "tags": ["russian", "classification", "emotion", "emotion-detection", "emotion-recognition", "multiclass"], "datasets": ["Djacon/ru-izard-emotions"], "widget": [{"text": "\u0421\u0435\u0439\u0447\u0430\u0441 \u0440\u043e\u0432\u043d\u043e \u0447\u0430\u0441 \u0434\u043d\u044f"}, {"text": "\u0421\u0435\u0433\u043e\u0434\u043d\u044f \u0442\u0430\u043a\u043e\u0439 \u0437\u0430\u043c\u0435\u0447\u0430\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0434\u0435\u043d\u044c!"}, {"text": "\u0416\u0430\u043b\u0435\u044e, \u0447\u0442\u043e \u0432\u0447\u0435\u0440\u0430 \u0441\u0445\u043e\u0434\u0438\u043b \u043d\u0430 \u044d\u0442\u043e\u0442 \u043a\u043e\u043d\u0446\u0435\u0440\u0442"}, {"text": "\u0427\u0442\u043e \u0437\u0430 \u0431\u0440\u0435\u0434 \u044f \u0442\u043e\u043b\u044c\u043a\u043e \u0447\u0442\u043e \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u043b..."}, {"text": "\u041a\u0443\u0434\u0430 \u0431\u044b \u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0441\u0445\u043e\u0434\u0438\u0442\u044c?"}, {"text": "\u0412\u043e\u0443, \u044d\u0442\u043e \u0431\u044b\u043b\u043e \u0442\u0430\u043a \u043d\u0435\u043e\u0436\u0438\u0434\u0430\u043d\u043d\u043e"}, {"text": "\u0424\u0443, \u044d\u0442\u0430 \u0435\u0434\u0430 \u043f\u0440\u043e\u0441\u0442\u043e \u043e\u0442\u0432\u0440\u0430\u0442\u0438\u0442\u0435\u043b\u044c\u043d\u0430!"}, {"text": "\u0412 \u0442\u0435\u043c\u043d\u043e\u0439 \u043a\u043e\u043c\u043d\u0430\u0442\u0435 \u0443\u0441\u043b\u044b\u0448\u0430\u043b \u0442\u0438\u0445\u0438\u0439 \u043f\u043e\u0441\u0442\u043e\u0440\u043e\u043d\u043d\u0438\u0439 \u0448\u043e\u0440\u043e\u0445"}, {"text": "\u0418\u0437\u0432\u0438\u043d\u0438, \u044f \u043d\u0435 \u0445\u043e\u0442\u0435\u043b \u0447\u0442\u043e\u0431\u044b \u0442\u0430\u043a \u0432\u0441\u0435 \u043f\u0440\u043e\u0438\u0437\u043e\u0448\u043b\u043e"}]}
transformers pytorch safetensors bert text-classification russian classification emotion emotion-detection emotion-recognition multiclass ru en dataset:Djacon/ru-izard-emotions license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #emotion #emotion-detection #emotion-recognition #multiclass #ru #en #dataset-Djacon/ru-izard-emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

Short Description
-----------------


The **rubert-tiny2-russian-emotion-detection** is a fine-tuned rubert-tiny2 model for multi-label **emotion classification** task, specifically on Russian texts. Trained on custom ru-izard-emotions dataset, so this model can recognize a spectrum of 9 emotions, including **joy**, **sadness**, **anger**, **enthusiasm**, **surprise**, **disgust**, **fear**, **guilt**, **shame** + **neutral** (no emotion). Project was inspired by the Izard's model of human emotions.


For more information about model, please check Github repository


Training Parameters:
--------------------


Emotion Categories:
-------------------


Test results:
-------------



s



TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #emotion #emotion-detection #emotion-recognition #multiclass #ru #en #dataset-Djacon/ru-izard-emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #emotion #emotion-detection #emotion-recognition #multiclass #ru #en #dataset-Djacon/ru-izard-emotions #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
