readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  model-index:
  - name: rubert-base-cased_best_finetuned_emotion_experiment_augmented_anger_fear
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # 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
  