readme: ---
  license: apache-2.0
  tags:
  - audio-classification
  - generated_from_trainer
  datasets:
  - superb
  metrics:
  - accuracy
  model-index:
  - name: wav2vec2-base-ks-linear_lrX100
    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-base-ks-linear_lrX100
  
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6970
  - Accuracy: 0.8001
  
  ## 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: 0.003
  - train_batch_size: 256
  - eval_batch_size: 256
  - seed: 0
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 1024
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.1789        | 1.0   | 50   | 1.3621          | 0.6225   |
  | 0.636         | 2.0   | 100  | 0.9176          | 0.6912   |
  | 0.5575        | 3.0   | 150  | 0.8543          | 0.7376   |
  | 0.5289        | 4.0   | 200  | 0.6970          | 0.8001   |
  | 0.4926        | 5.0   | 250  | 0.8232          | 0.7548   |
  | 0.4831        | 6.0   | 300  | 0.7442          | 0.7755   |
  | 0.4539        | 7.0   | 350  | 0.7484          | 0.7785   |
  | 0.4816        | 8.0   | 400  | 0.7038          | 0.7982   |
  | 0.4666        | 9.0   | 450  | 0.7277          | 0.7764   |
  | 0.4417        | 10.0  | 500  | 0.7289          | 0.7870   |
  
  
  ### Framework versions
  
  - Transformers 4.22.0.dev0
  - Pytorch 1.11.0+cu115
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  