LABEL_0 LABEL_1 LABEL_2 LABEL_3
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: rubert-base-cased_best_finetuned_emotion_experiment_augmented_anger_fear
  results: []
---

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# rubert-base-cased_best_finetuned_emotion_experiment_augmented_anger_fear

This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4568
- Accuracy: 0.8779
- F1: 0.8777
- Precision: 0.8780
- Recall: 0.8779

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=0.0001
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.2647        | 1.0   | 69   | 1.0075          | 0.6013   | 0.5671 | 0.6594    | 0.6013 |
| 0.9091        | 2.0   | 138  | 0.7853          | 0.7171   | 0.7138 | 0.7169    | 0.7171 |
| 0.7305        | 3.0   | 207  | 0.6264          | 0.7829   | 0.7811 | 0.7835    | 0.7829 |
| 0.5446        | 4.0   | 276  | 0.4571          | 0.8466   | 0.8465 | 0.8470    | 0.8466 |
| 0.4039        | 5.0   | 345  | 0.4035          | 0.8612   | 0.8606 | 0.8612    | 0.8612 |
| 0.3144        | 6.0   | 414  | 0.3800          | 0.8653   | 0.8653 | 0.8665    | 0.8653 |
| 0.2711        | 7.0   | 483  | 0.3731          | 0.8674   | 0.8673 | 0.8677    | 0.8674 |
| 0.2289        | 8.0   | 552  | 0.4041          | 0.8737   | 0.8728 | 0.8746    | 0.8737 |
| 0.1944        | 9.0   | 621  | 0.4002          | 0.8789   | 0.8785 | 0.8793    | 0.8789 |
| 0.171         | 10.0  | 690  | 0.3939          | 0.8831   | 0.8827 | 0.8839    | 0.8831 |
| 0.138         | 11.0  | 759  | 0.4106          | 0.8758   | 0.8754 | 0.8761    | 0.8758 |
| 0.1141        | 12.0  | 828  | 0.4200          | 0.8810   | 0.8803 | 0.8804    | 0.8810 |
| 0.1141        | 13.0  | 897  | 0.4426          | 0.8758   | 0.8756 | 0.8763    | 0.8758 |
| 0.0961        | 14.0  | 966  | 0.4494          | 0.8758   | 0.8754 | 0.8761    | 0.8758 |
| 0.0812        | 15.0  | 1035 | 0.4568          | 0.8779   | 0.8777 | 0.8780    | 0.8779 |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
