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seara
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rubert-tiny2-russian-emotion-detection-ru-go-emotions

Text Classification
Transformers
PyTorch
TensorFlow
JAX
Safetensors
Russian
bert
sentiment-analysis
multi-label-classification
sentiment analysis
rubert
sentiment
tiny
russian
multilabel
classification
emotion-classification
emotion-recognition
emotion
emotion-detection
Inference Endpoints
Model card Files Community
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  ---
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  license: mit
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- datasets:
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- - seara/ru_go_emotions
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  language:
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  - ru
 
 
 
 
 
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  pipeline_tag: text-classification
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: mit
 
 
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  language:
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  - ru
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+ metrics:
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+ - f1
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+ - roc_auc
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+ - precision
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+ - recall
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  pipeline_tag: text-classification
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+ tags:
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+ - sentiment-analysis
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+ - multi-label-classification
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+ - sentiment analysis
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+ - rubert
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+ - sentiment
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+ - bert
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+ - tiny
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+ - russian
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+ - multilabel
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+ - classification
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+ - emotion-classification
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+ - emotion-recognition
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+ - emotion
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+ datasets:
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+ - seara/ru_go_emotions
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+ - go_emotions
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+ ---
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+
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+ This is [RuBERT-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) model fine-tuned for __emotion classification__ of short __Russian__ texts.
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+ The task is a __multi-label classification__ with the following labels:
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+
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+ ```yaml
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+ 0: admiration
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+ 1: amusement
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+ 2: anger
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+ 3: annoyance
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+ 4: approval
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+ 5: caring
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+ 6: confusion
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+ 7: curiosity
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+ 8: desire
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+ 9: disappointment
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+ 10: disapproval
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+ 11: disgust
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+ 12: embarrassment
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+ 13: excitement
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+ 14: fear
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+ 15: gratitude
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+ 16: grief
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+ 17: joy
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+ 18: love
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+ 19: nervousness
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+ 20: optimism
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+ 21: pride
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+ 22: realization
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+ 23: relief
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+ 24: remorse
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+ 25: sadness
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+ 26: surprise
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+ 27: neutral
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+ ```
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+
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+ Label to Russian label:
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+
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+ ```yaml
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+ admiration: восхищение
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+ amusement: веселье
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+ anger: злость
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+ annoyance: раздражение
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+ approval: одобрение
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+ caring: забота
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+ confusion: непонимание
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+ curiosity: любопытство
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+ desire: желание
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+ disappointment: разочарование
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+ disapproval: неодобрение
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+ disgust: отвращение
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+ embarrassment: смущение
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+ excitement: возбуждение
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+ fear: страх
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+ gratitude: признательность
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+ grief: горе
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+ joy: радость
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+ love: любовь
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+ nervousness: нервозность
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+ optimism: оптимизм
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+ pride: гордость
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+ realization: осознание
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+ relief: облегчение
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+ remorse: раскаяние
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+ sadness: грусть
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+ surprise: удивление
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+ neutral: нейтральность
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+ ```
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+
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+ ## Usage
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+
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+ ```python
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+ from transformers import pipeline
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+ model = pipeline(model="seara/rubert-tiny2-ru-go-emotions")
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+ model("Привет, ты мне нравишься!")
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+ # [{'label': 'love', 'score': 0.5955629944801331}]
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+ ```
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+
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+ ## Dataset
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+
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+ This model was trained on translated GoEmotions dataset called [Ru-GoEmotions](https://huggingface.co/datasets/seara/ru_go_emotions).
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+
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+ An overview of the training data can be found on Hugging Face card and on [Github repository](https://github.com/searayeah/Ru-GoEmotions).
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+
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+ ## Training
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+
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+ Training were done in this [project](https://github.com/searayeah/vkr-bert) with this parameters:
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+
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+ ```yaml
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+ tokenizer.max_length: null
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+ batch_size: 64
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+ optimizer: adam
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+ lr: 0.00001
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+ weight_decay: 0
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+ num_epochs: 31
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+ ```
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+
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+ ## Eval results (on test split)
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+
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+ | |precision|recall|f1-score|auc-roc|support|
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+ |--------------|---------|------|--------|-------|-------|
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+ |admiration |0.68 |0.61 |0.64 |0.92 |504 |
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+ |amusement |0.8 |0.84 |0.82 |0.96 |264 |
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+ |anger |0.55 |0.33 |0.42 |0.9 |198 |
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+ |annoyance |0.56 |0.03 |0.06 |0.81 |320 |
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+ |approval |0.6 |0.18 |0.28 |0.78 |351 |
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+ |caring |0.5 |0.04 |0.07 |0.84 |135 |
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+ |confusion |0.77 |0.07 |0.12 |0.9 |153 |
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+ |curiosity |0.51 |0.34 |0.41 |0.92 |284 |
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+ |desire |0.71 |0.18 |0.29 |0.88 |83 |
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+ |disappointment|0.0 |0.0 |0.0 |0.76 |151 |
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+ |disapproval |0.48 |0.1 |0.17 |0.85 |267 |
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+ |disgust |0.94 |0.12 |0.22 |0.9 |123 |
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+ |embarrassment |0.0 |0.0 |0.0 |0.84 |37 |
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+ |excitement |0.81 |0.2 |0.33 |0.88 |103 |
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+ |fear |0.73 |0.42 |0.54 |0.92 |78 |
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+ |gratitude |0.95 |0.89 |0.92 |0.99 |352 |
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+ |grief |0.0 |0.0 |0.0 |0.76 |6 |
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+ |joy |0.66 |0.52 |0.58 |0.93 |161 |
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+ |love |0.8 |0.79 |0.79 |0.97 |238 |
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+ |nervousness |0.0 |0.0 |0.0 |0.81 |23 |
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+ |optimism |0.67 |0.41 |0.51 |0.89 |186 |
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+ |pride |0.0 |0.0 |0.0 |0.89 |16 |
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+ |realization |0.0 |0.0 |0.0 |0.7 |145 |
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+ |relief |0.0 |0.0 |0.0 |0.84 |11 |
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+ |remorse |0.59 |0.71 |0.65 |0.99 |56 |
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+ |sadness |0.77 |0.37 |0.5 |0.89 |156 |
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+ |surprise |0.59 |0.35 |0.44 |0.88 |141 |
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+ |neutral |0.64 |0.58 |0.61 |0.81 |1787 |
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+ |micro avg |0.68 |0.43 |0.53 |0.93 |6329 |
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+ |macro avg |0.51 |0.29 |0.33 |0.87 |6329 |
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+ |weighted avg |0.62 |0.43 |0.48 |0.86 |6329 |