readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: wav2vec2-xlsr-korean-speech-emotion-recognition2_data_rebalance
    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. -->
  
  # wav2vec2-xlsr-korean-speech-emotion-recognition2_data_rebalance
  
  This model is a fine-tuned version of [jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15](https://huggingface.co/jungjongho/wav2vec2-large-xlsr-korean-demo-colab_epoch15) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0124
  - Accuracy: 0.9976
  
  ## 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: 0.0002
  - train_batch_size: 2
  - eval_batch_size: 2
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.1219        | 0.04  | 200  | 1.4543          | 0.5792   |
  | 0.7585        | 0.08  | 400  | 0.6959          | 0.7301   |
  | 0.5959        | 0.13  | 600  | 0.7356          | 0.7671   |
  | 0.4048        | 0.17  | 800  | 0.2661          | 0.9128   |
  | 0.3101        | 0.21  | 1000 | 0.2470          | 0.9291   |
  | 0.2805        | 0.25  | 1200 | 0.2115          | 0.9394   |
  | 0.2885        | 0.29  | 1400 | 0.3597          | 0.9152   |
  | 0.1703        | 0.33  | 1600 | 0.3973          | 0.9142   |
  | 0.1818        | 0.38  | 1800 | 0.2164          | 0.9543   |
  | 0.1727        | 0.42  | 2000 | 0.1133          | 0.9730   |
  | 0.1038        | 0.46  | 2200 | 0.0826          | 0.9851   |
  | 0.1343        | 0.5   | 2400 | 0.0823          | 0.9820   |
  | 0.1412        | 0.54  | 2600 | 0.0762          | 0.9830   |
  | 0.1321        | 0.58  | 2800 | 0.0786          | 0.9806   |
  | 0.0738        | 0.63  | 3000 | 0.1036          | 0.9810   |
  | 0.0998        | 0.67  | 3200 | 0.1984          | 0.9640   |
  | 0.1135        | 0.71  | 3400 | 0.0775          | 0.9841   |
  | 0.0552        | 0.75  | 3600 | 0.0923          | 0.9827   |
  | 0.0633        | 0.79  | 3800 | 0.0518          | 0.9900   |
  | 0.0769        | 0.83  | 4000 | 0.0599          | 0.9875   |
  | 0.1026        | 0.88  | 4200 | 0.0800          | 0.9841   |
  | 0.0641        | 0.92  | 4400 | 0.2396          | 0.9606   |
  | 0.1068        | 0.96  | 4600 | 0.0653          | 0.9875   |
  | 0.0802        | 1.0   | 4800 | 0.0844          | 0.9855   |
  | 0.0483        | 1.04  | 5000 | 0.0984          | 0.9834   |
  | 0.0392        | 1.09  | 5200 | 0.1092          | 0.9813   |
  | 0.0408        | 1.13  | 5400 | 0.0719          | 0.9900   |
  | 0.0388        | 1.17  | 5600 | 0.0494          | 0.9903   |
  | 0.0253        | 1.21  | 5800 | 0.1486          | 0.9751   |
  | 0.0448        | 1.25  | 6000 | 0.1370          | 0.9782   |
  | 0.0415        | 1.29  | 6200 | 0.0508          | 0.9907   |
  | 0.0552        | 1.34  | 6400 | 0.0332          | 0.9941   |
  | 0.065         | 1.38  | 6600 | 0.0479          | 0.9900   |
  | 0.0391        | 1.42  | 6800 | 0.0470          | 0.9910   |
  | 0.0339        | 1.46  | 7000 | 0.0550          | 0.9886   |
  | 0.0525        | 1.5   | 7200 | 0.0389          | 0.9920   |
  | 0.0393        | 1.54  | 7400 | 0.0543          | 0.9910   |
  | 0.0488        | 1.59  | 7600 | 0.0205          | 0.9965   |
  | 0.0253        | 1.63  | 7800 | 0.0240          | 0.9948   |
  | 0.0438        | 1.67  | 8000 | 0.0308          | 0.9952   |
  | 0.0291        | 1.71  | 8200 | 0.0160          | 0.9969   |
  | 0.0235        | 1.75  | 8400 | 0.0124          | 0.9969   |
  | 0.0061        | 1.8   | 8600 | 0.0191          | 0.9962   |
  | 0.022         | 1.84  | 8800 | 0.0178          | 0.9958   |
  | 0.0176        | 1.88  | 9000 | 0.0135          | 0.9965   |
  | 0.0168        | 1.92  | 9200 | 0.0161          | 0.9969   |
  | 0.0068        | 1.96  | 9400 | 0.0124          | 0.9976   |
  
  
  ### Framework versions
  
  - Transformers 4.22.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.1.dev0
  - Tokenizers 0.12.1
  