This is RuBERT model fine-tuned for emotion classification of short Russian texts. The task is a multi-label classification with the following labels:
0: admiration
1: amusement
2: anger
3: annoyance
4: approval
5: caring
6: confusion
7: curiosity
8: desire
9: disappointment
10: disapproval
11: disgust
12: embarrassment
13: excitement
14: fear
15: gratitude
16: grief
17: joy
18: love
19: nervousness
20: optimism
21: pride
22: realization
23: relief
24: remorse
25: sadness
26: surprise
27: neutral
Label to Russian label:
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
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:
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 |
- Downloads last month
- 4,072
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.