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:
|
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
|