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seara/
rubert-base-cased-russian-emotion-detection-ru-go-emotions

Name: rubert-base-cased-russian-emotion-detection-ru-go-emotions
Availability: InStock

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This is RuBERT model fine-tuned for emotion classification of short Russian texts. The task is a multi-label classification with the following labels:

yaml

admiration
amusement
anger
annoyance
approval
caring
confusion
curiosity
desire
disappointment
disapproval
disgust
embarrassment
excitement
fear
gratitude
grief
joy
love
nervousness
optimism
pride
realization
relief
remorse
sadness
surprise
neutral

Label to Russian label:

yaml

admiration: восхищение
amusement: веселье
anger: злость
annoyance: раздражение
approval: одобрение
caring: забота
confusion: непонимание
curiosity: любопытство
desire: желание
disappointment: разочарование
disapproval: неодобрение
disgust: отвращение
embarrassment: смущение
excitement: возбуждение
fear: страх
gratitude: признательность
grief: горе
joy: радость
love: любовь
nervousness: нервозность
optimism: оптимизм
pride: гордость
realization: осознание
relief: облегчение
remorse: раскаяние
sadness: грусть
surprise: удивление
neutral: нейтральность

Usage

python

from transformers import pipeline
model = pipeline(model="seara/rubert-base-cased-ru-go-emotions")
model("Привет, ты мне нравишься!")
# [{'label': 'love', 'score': 0.5456761717796326}]

Dataset

This model was trained on translated GoEmotions dataset called ru_go_emotions.

An overview of the training data can be found on Hugging Face card and on Github repository.

Training

Training were done in this project with this parameters:

yaml

tokenizer.max_length: null
batch_size: 32
optimizer: adam
lr: 0.00001
weight_decay: 0
num_epochs: 5

Eval results (on test split)

	precision	recall	f1-score	auc-roc	support
admiration	0.66	0.66	0.66	0.93	504
amusement	0.79	0.81	0.8	0.97	264
anger	0.53	0.3	0.39	0.91	198
annoyance	0.0	0.0	0.0	0.82	320
approval	0.62	0.25	0.36	0.82	351
caring	0.69	0.13	0.22	0.86	135
confusion	0.56	0.18	0.28	0.92	153
curiosity	0.52	0.4	0.45	0.95	284
desire	0.67	0.24	0.35	0.89	83
disappointment	0.88	0.05	0.09	0.82	151
disapproval	0.56	0.17	0.26	0.88	267
disgust	0.83	0.2	0.33	0.92	123
embarrassment	0.0	0.0	0.0	0.88	37
excitement	0.78	0.14	0.23	0.9	103
fear	0.83	0.37	0.51	0.92	78
gratitude	0.94	0.9	0.92	0.99	352
grief	0.0	0.0	0.0	0.72	6
joy	0.7	0.4	0.51	0.94	161
love	0.77	0.81	0.79	0.97	238
nervousness	0.0	0.0	0.0	0.85	23
optimism	0.66	0.52	0.58	0.92	186
pride	0.0	0.0	0.0	0.76	16
realization	0.0	0.0	0.0	0.74	145
relief	0.0	0.0	0.0	0.72	11
remorse	0.58	0.68	0.63	0.99	56
sadness	0.58	0.44	0.5	0.92	156
surprise	0.62	0.45	0.52	0.91	141
neutral	0.72	0.47	0.57	0.84	1787
micro avg	0.7	0.42	0.53	0.94	6329
macro avg	0.52	0.31	0.36	0.88	6329
weighted avg	0.63	0.42	0.49	0.88	6329

seara/rubert-base-cased-russian-emotion-detection-ru-go-emotions

Usage

Dataset

Training

Eval results (on test split)

seara/
rubert-base-cased-russian-emotion-detection-ru-go-emotions