This is the [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for classification of emotions in Russian sentences. The task is multilabel classification, because one sentence can contain multiple emotions.

The model on the [CEDR dataset](https://huggingface.co/datasets/cedr) described in the paper ["Data-Driven Model for Emotion Detection in Russian Texts"](https://doi.org/10.1016/j.procs.2021.06.075) by Sboev et al.

The model has been trained with Adam optimizer for 40 epochs with learning rate `1e-5` and batch size 64 [in this notebook](https://colab.research.google.com/drive/1AFW70EJaBn7KZKRClDIdDUpbD46cEsat?usp=sharing).

The quality of the predicted probabilities on the test dataset is the following:

| label    | no emotion | joy    |sadness |surprise| fear   |anger   | mean    | mean (emotions) |
|----------|------------|--------|--------|--------|--------|--------| --------| ----------------|
| AUC      | 0.9286     | 0.9512 | 0.9564 | 0.8908 | 0.8955 | 0.7511 |  0.8956 | 0.8890          |
| F1 micro | 0.8624     | 0.9389 | 0.9362 | 0.9469 | 0.9575 | 0.9261 |  0.9280 | 0.9411          |
| F1 macro | 0.8562     | 0.8962 | 0.9017 | 0.8366 | 0.8359 | 0.6820 |  0.8348 | 0.8305          |

{"language": ["ru"], "tags": ["russian", "classification", "sentiment", "emotion-classification", "multiclass"], "datasets": ["cedr"], "widget": [{"text": "\u0411\u0435\u0441\u0438\u0448\u044c \u043c\u0435\u043d\u044f, \u043f\u0430\u0434\u043b\u0430"}, {"text": "\u041a\u0430\u043a \u0437\u0434\u043e\u0440\u043e\u0432\u043e, \u0447\u0442\u043e \u0432\u0441\u0435 \u043c\u044b \u0437\u0434\u0435\u0441\u044c \u0441\u0435\u0433\u043e\u0434\u043d\u044f \u0441\u043e\u0431\u0440\u0430\u043b\u0438\u0441\u044c"}, {"text": "\u041a\u0430\u043a-\u0442\u043e \u0441\u0442\u0440\u0451\u043c\u043d\u043e, \u0434\u0430\u0432\u0430\u0439 \u0441\u0432\u0430\u043b\u0438\u043c \u043e\u0442\u0441\u044e\u0434\u0430?"}, {"text": "\u0413\u0440\u0443\u0441\u0442\u044c-\u0442\u043e\u0441\u043a\u0430 \u043c\u0435\u043d\u044f \u0441\u044a\u0435\u0434\u0430\u0435\u0442"}, {"text": "\u0414\u0430\u043d\u043d\u044b\u0439 \u0444\u0440\u0430\u0433\u043c\u0435\u043d\u0442 \u0442\u0435\u043a\u0441\u0442\u0430 \u043d\u0435 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u043e \u043d\u0438\u043a\u0430\u043a\u0438\u0445 \u044d\u043c\u043e\u0446\u0438\u0439"}, {"text": "\u041d\u0438\u0444\u0438\u0433\u0430 \u0441\u0435\u0431\u0435, \u043d\u0435\u0443\u0436\u0435\u043b\u0438 \u0442\u0430\u043a \u0442\u043e\u0436\u0435 \u0431\u044b\u0432\u0430\u0435\u0442!"}]}
transformers pytorch safetensors bert text-classification russian classification sentiment emotion-classification multiclass ru dataset:cedr autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #emotion-classification #multiclass #ru #dataset-cedr #autotrain_compatible #endpoints_compatible #has_space #region-us 

This is the cointegrated/rubert-tiny2 model fine-tuned for classification of emotions in Russian sentences. The task is multilabel classification, because one sentence can contain multiple emotions.


The model on the CEDR dataset described in the paper "Data-Driven Model for Emotion Detection in Russian Texts" by Sboev et al.


The model has been trained with Adam optimizer for 40 epochs with learning rate '1e-5' and batch size 64 in this notebook.


The quality of the predicted probabilities on the test dataset is the following:




TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #emotion-classification #multiclass #ru #dataset-cedr #autotrain_compatible #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #emotion-classification #multiclass #ru #dataset-cedr #autotrain_compatible #endpoints_compatible #has_space #region-us 
