readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: wav2vec2-large-xls-r-300m-dm32
    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-large-xls-r-300m-dm32
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5688
  - Accuracy: 0.7917
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 22
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 2.41  | 34   | 0.6769          | 0.6458   |
  | No log        | 4.83  | 68   | 0.6864          | 0.5208   |
  | No log        | 7.28  | 102  | 0.6596          | 0.6042   |
  | 0.7106        | 9.69  | 136  | 0.6208          | 0.6875   |
  | 0.7106        | 12.14 | 170  | 0.6152          | 0.6875   |
  | 0.7106        | 14.55 | 204  | 0.6167          | 0.6875   |
  | 0.6464        | 16.97 | 238  | 0.5782          | 0.7708   |
  | 0.6464        | 19.41 | 272  | 0.6011          | 0.7292   |
  | 0.6464        | 21.83 | 306  | 0.5688          | 0.7917   |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  