Create README.md
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README.md
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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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---
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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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```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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Label to Russian label:
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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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## Usage
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```python
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from transformers import pipeline
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model = pipeline(model="r1char9/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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model('Hello, I love you.')
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# [{'label': 'love', 'score': 0.911257266998291}]
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```
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