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rubert-tiny2-cedr-emotion-detection

Text Classification
Transformers
PyTorch
Safetensors
Russian
bert
russian
classification
sentiment
emotion-classification
multiclass
Inference Endpoints
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@@ -25,8 +25,8 @@ The model has been trained with Adam optimizer for 40 epochs with learning rate
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  ROC AUC of the predicted probabilities on the test dataset is the following:
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- | label | no emotion | joy |sadness |surprise| fear |anger | mean |
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- |----------|------------|--------|--------|--------|--------|--------| --------|
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- | AUC | 0.9406 | 0.9518 | 0.9372 | 0.8634 | 0.9663 | 0.6761 | 0.8892 |
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- | F1 micro | 0.8624 | 0.9389 | 0.9362 | 0.9469 | 0.9575 | 0.9261 | 0.9280 |
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- | F1 macro | 0.8562 | 0.8962 | 0.9017 | 0.8366 | 0.8359 | 0.6820 | 0.8348 |
 
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  ROC AUC of the predicted probabilities on the test dataset is the following:
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+ | label | no emotion | joy |sadness |surprise| fear |anger | mean | mean (emotions) |
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+ |----------|------------|--------|--------|--------|--------|--------| --------| ----------------|
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+ | AUC | 0.9406 | 0.9518 | 0.9372 | 0.8634 | 0.9663 | 0.6761 | 0.8892 | 0.8890 |
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+ | F1 micro | 0.8624 | 0.9389 | 0.9362 | 0.9469 | 0.9575 | 0.9261 | 0.9280 | 0.9411 |
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+ | F1 macro | 0.8562 | 0.8962 | 0.9017 | 0.8366 | 0.8359 | 0.6820 | 0.8348 | 0.8305 |