readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: hubert-base-ser
    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. -->
  
  # hubert-base-ser
  
  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on the Crema dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0105
  - Accuracy: 0.6313
  
  ## 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: 0.0001
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.8106        | 0.01  | 10   | 1.7616          | 0.1974   |
  | 1.7268        | 0.03  | 20   | 1.7187          | 0.2525   |
  | 1.7269        | 0.04  | 30   | 1.6442          | 0.3096   |
  | 1.7086        | 0.05  | 40   | 1.5834          | 0.3338   |
  | 1.6983        | 0.07  | 50   | 1.6195          | 0.3600   |
  | 1.5845        | 0.08  | 60   | 1.5753          | 0.3418   |
  | 1.5744        | 0.09  | 70   | 1.5669          | 0.3707   |
  | 1.5915        | 0.11  | 80   | 1.5412          | 0.3754   |
  | 1.5105        | 0.12  | 90   | 2.0037          | 0.2612   |
  | 1.4689        | 0.13  | 100  | 1.5440          | 0.3627   |
  | 1.527         | 0.15  | 110  | 1.5400          | 0.3862   |
  | 1.6481        | 0.16  | 120  | 1.6678          | 0.3298   |
  | 1.7504        | 0.17  | 130  | 1.6078          | 0.2995   |
  | 1.3748        | 0.19  | 140  | 1.5750          | 0.3251   |
  | 1.6417        | 0.2   | 150  | 1.7034          | 0.2599   |
  | 1.6146        | 0.21  | 160  | 1.6162          | 0.3519   |
  | 1.4896        | 0.23  | 170  | 1.5245          | 0.3741   |
  | 1.4278        | 0.24  | 180  | 1.7537          | 0.2424   |
  | 1.4475        | 0.26  | 190  | 1.4769          | 0.3882   |
  | 1.5416        | 0.27  | 200  | 1.4772          | 0.3949   |
  | 1.5997        | 0.28  | 210  | 1.4428          | 0.4278   |
  | 1.4337        | 0.3   | 220  | 1.4352          | 0.4124   |
  | 1.415         | 0.31  | 230  | 1.4405          | 0.4157   |
  | 1.5196        | 0.32  | 240  | 1.4197          | 0.4043   |
  | 1.3866        | 0.34  | 250  | 1.5241          | 0.3734   |
  | 1.3041        | 0.35  | 260  | 1.5703          | 0.4043   |
  | 1.3618        | 0.36  | 270  | 1.3963          | 0.4285   |
  | 1.3293        | 0.38  | 280  | 1.3478          | 0.4506   |
  | 1.2215        | 0.39  | 290  | 1.5994          | 0.3842   |
  | 1.6618        | 0.4   | 300  | 1.7751          | 0.2277   |
  | 1.5349        | 0.42  | 310  | 1.6091          | 0.4036   |
  | 1.4037        | 0.43  | 320  | 1.4741          | 0.4446   |
  | 1.4844        | 0.44  | 330  | 1.4170          | 0.4399   |
  | 1.2806        | 0.46  | 340  | 1.2887          | 0.5050   |
  | 1.3818        | 0.47  | 350  | 1.2668          | 0.5017   |
  | 1.3491        | 0.48  | 360  | 1.4721          | 0.4594   |
  | 1.2347        | 0.5   | 370  | 1.2188          | 0.5245   |
  | 1.2182        | 0.51  | 380  | 1.3813          | 0.4567   |
  | 1.2513        | 0.52  | 390  | 1.2111          | 0.5205   |
  | 1.2447        | 0.54  | 400  | 1.2231          | 0.5460   |
  | 1.038         | 0.55  | 410  | 1.2563          | 0.5373   |
  | 1.2409        | 0.56  | 420  | 1.3448          | 0.4936   |
  | 1.2279        | 0.58  | 430  | 1.1972          | 0.5487   |
  | 1.3256        | 0.59  | 440  | 1.1706          | 0.5742   |
  | 1.2866        | 0.6   | 450  | 1.3091          | 0.5003   |
  | 1.0574        | 0.62  | 460  | 1.2075          | 0.5500   |
  | 1.2744        | 0.63  | 470  | 1.2831          | 0.5171   |
  | 1.0836        | 0.64  | 480  | 1.1768          | 0.5608   |
  | 1.135         | 0.66  | 490  | 1.1408          | 0.5776   |
  | 1.1303        | 0.67  | 500  | 1.2320          | 0.5541   |
  | 1.2068        | 0.69  | 510  | 1.1379          | 0.5796   |
  | 1.1347        | 0.7   | 520  | 1.1124          | 0.5897   |
  | 1.1846        | 0.71  | 530  | 1.1338          | 0.5803   |
  | 1.2409        | 0.73  | 540  | 1.1259          | 0.5789   |
  | 1.0664        | 0.74  | 550  | 1.0653          | 0.6038   |
  | 1.1637        | 0.75  | 560  | 1.0550          | 0.5977   |
  | 1.0707        | 0.77  | 570  | 1.0996          | 0.5715   |
  | 1.2258        | 0.78  | 580  | 1.0804          | 0.5977   |
  | 0.9256        | 0.79  | 590  | 1.1501          | 0.5809   |
  | 1.1542        | 0.81  | 600  | 1.1089          | 0.5957   |
  | 1.3931