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rubert-tiny2-russian-emotion-detection-cedr

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
text-embeddings-inference
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@@ -9,14 +9,18 @@ metrics:
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  - recall
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  pipeline_tag: text-classification
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  tags:
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- - rubert
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- - emotion
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- - sentiment
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  - emotion-classification
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  - emotion-recognition
 
 
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  - russian
 
 
 
 
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  - classification
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  - multilabel
 
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  datasets:
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  - cedr
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  ---
@@ -33,6 +37,17 @@ The task is a __multi-label classification__ with the following labels:
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  5: anger
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  ```
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  ## Usage
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  ```python
@@ -63,10 +78,10 @@ num_epochs: 30
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  ## Eval results (on test split)
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- | |no_emotion|joy |sadness|surprise|fear |anger |micro avg|macro avg|weighted avg|samples avg|
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- |---------|----------|------|-------|--------|-------|------|---------|---------|------------|-----------|
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- |precision|0.8176 |0.8371|0.8425 |0.7902 |0.7833 |0.5467|0.811 |0.7696 |0.8034 |0.7811 |
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- |recall |0.8365 |0.83 |0.847 |0.6647 |0.6667 |0.328 |0.776 |0.6955 |0.776 |0.7792 |
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- |f1-score |0.8269 |0.8336|0.8447 |0.722 |0.7203 |0.41 |0.7931 |0.7263 |0.787 |0.7788 |
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- |support |734.0 |353.0 |379.0 |170.0 |141.0 |125.0 |1902.0 |1902.0 |1902.0 |1902.0 |
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- |auc-roc |0.9241 |0.9649|0.9557 |0.913 |0.9118 |0.7732|0.9355 |0.9071 |0.9261 | |
 
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  - recall
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  pipeline_tag: text-classification
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  tags:
 
 
 
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  - emotion-classification
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  - emotion-recognition
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+ - multi-label-classification
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+ - emotion
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  - russian
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+ - rubert
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+ - tiny
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+ - sentiment
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+ - sentiment-analysis
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  - classification
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  - multilabel
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+ - bert
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  datasets:
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  - cedr
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  ---
 
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  5: anger
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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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+ no_emotion: нет эмоции
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+ joy: радость
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+ sadness: грусть
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+ surprise: удивление
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+ fear: страх
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+ anger: злость
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+ ```
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+
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  ## Usage
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  ```python
 
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  ## Eval results (on test split)
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+ | |no_emotion|joy |sadness|surprise|fear |anger|micro avg|macro avg|weighted avg|
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+ |---------|----------|------|-------|--------|-------|-----|---------|---------|------------|
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+ |precision|0.82 |0.84 |0.84 |0.79 |0.78 |0.55 |0.81 |0.77 |0.8 |
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+ |recall |0.84 |0.83 |0.85 |0.66 |0.67 |0.33 |0.78 |0.7 |0.78 |
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+ |f1-score |0.83 |0.83 |0.84 |0.72 |0.72 |0.41 |0.79 |0.73 |0.79 |
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+ |auc-roc |0.92 |0.96 |0.96 |0.91 |0.91 |0.77 |0.94 |0.91 |0.93 |
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+ |support |734 |353 |379 |170 |141 |125 |1902 |1902 |1902 |