sentiment emotion-classification multilabel multiclass

ruBert-base-russian-emotions-classifier-goEmotions

This model is a fine-tuned version of ai-forever/ruBert-base on Djacon/ru_goemotions. It achieves the following results on the evaluation set (2nd epoch):

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

label joy interest surpise sadness anger disgust fear guilt neutral average
AUC 0.9369 0.9213 0.9325 0.8791 0.8374 0.9041 0.9470 0.9758 0.8518 0.9095
F1-micro 0.9528 0.9157 0.9697 0.9284 0.8690 0.9658 0.9851 0.9875 0.7654 0.9266
F1-macro 0.8369 0.7922 0.7561 0.7392 0.7351 0.7356 0.8176 0.8247 0.7650 0.7781

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

Training results

Training Loss Epoch Step Validation Loss AUC
0.1755 1.0 1685 0.1717 0.9220
0.1391 2.0 3370 0.1757 0.9240
0.0899 3.0 5055 0.2088 0.9106

Framework versions