readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: Nystrom-W2V2-100hrs-take-3
    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. -->
  
  # Nystrom-W2V2-100hrs-take-3
  
  This model was trained from scratch on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 30.1649
  - Wer: 0.1047
  
  ## 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: 0.0005
  - train_batch_size: 4
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 64
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_ratio: 0.2
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 55.4504       | 9.01  | 1000  | 29.0675         | 0.1464 |
  | 65.4799       | 18.02 | 2000  | 26.9263         | 0.1580 |
  | 72.6609       | 27.03 | 3000  | 27.2220         | 0.1500 |
  | 65.6264       | 36.04 | 4000  | 26.4758         | 0.1426 |
  | 57.9496       | 45.04 | 5000  | 27.0818         | 0.1349 |
  | 49.6643       | 54.05 | 6000  | 27.9658         | 0.1269 |
  | 42.5205       | 63.06 | 7000  | 28.6973         | 0.1214 |
  | 36.1799       | 72.07 | 8000  | 28.1021         | 0.1128 |
  | 30.9742       | 81.08 | 9000  | 29.9000         | 0.1093 |
  | 27.4728       | 90.09 | 10000 | 30.2661         | 0.1057 |
  | 26.0383       | 99.1  | 11000 | 30.1649         | 0.1047 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.11.0
  