metadata
language:
- ru
license: apache-2.0
tags:
- sentiment
- emotion-classification
- multilabel
- multiclass
datasets:
- Djacon/ru_goemotions
metrics:
- accuracy
widget:
- text: Очень рад тебя видеть!
- text: Как дела?
- text: Мне немного отвратно это делать
- text: Я испытал мурашки от страха
- text: Нет ничего радостного в этих горьких новостях
- text: Ого, неожидал тебя здесь увидеть!
- text: Фу ну и мерзость
- text: Мне неприятно общение с тобой
base_model: ai-forever/ruBert-base
model-index:
- name: ruBert-base-russian-emotions-classifier-goEmotions
results:
- task:
type: multilabel-text-classification
name: Multilabel Text Classification
dataset:
name: ru_goemotions
type: Djacon/ru_goemotions
args: ru
metrics:
- type: roc_auc
value: 92%
name: multilabel ROC AUC
ruBert-base-russian-emotions-classifier-goEmotions
This model is a fine-tuned version of ai-forever/ruBert-base on Djacon/ru_goemotions. It achieves the following results on the evaluation set (2nd epoch):
- Loss: 0.2088
- AUC: 0.9240
The quality of the predicted probabilities on the test dataset is the following:
| label | joy | interest | surpise | sadness | anger | disgust | fear | guilt | neutral | average |
|---|---|---|---|---|---|---|---|---|---|---|
| AUC | 0.9369 | 0.9213 | 0.9325 | 0.8791 | 0.8374 | 0.9041 | 0.9470 | 0.9758 | 0.8518 | 0.9095 |
| F1-micro | 0.9528 | 0.9157 | 0.9697 | 0.9284 | 0.8690 | 0.9658 | 0.9851 | 0.9875 | 0.7654 | 0.9266 |
| F1-macro | 0.8369 | 0.7922 | 0.7561 | 0.7392 | 0.7351 | 0.7356 | 0.8176 | 0.8247 | 0.7650 | 0.7781 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | AUC |
|---|---|---|---|---|
| 0.1755 | 1.0 | 1685 | 0.1717 | 0.9220 |
| 0.1391 | 2.0 | 3370 | 0.1757 | 0.9240 |
| 0.0899 | 3.0 | 5055 | 0.2088 | 0.9106 |
Framework versions
- Transformers 4.24.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.11.0