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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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@@ -27,6 +27,6 @@ The quality 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 |
 
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  | label | no emotion | joy |sadness |surprise| fear |anger | mean | mean (emotions) |
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  |----------|------------|--------|--------|--------|--------|--------| --------| ----------------|
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+ | AUC | 0.9286 | 0.9512 | 0.9564 | 0.8908 | 0.8955 | 0.7511 | 0.8956 | 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 |