---
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](https://huggingface.co/ai-forever/ruBert-base) on [Djacon/ru_goemotions](https://huggingface.co/datasets/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