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

The following hyperparameters were used during training:

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

MaxKazak/ruBert-base-russian-emotion-detection