readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: Nystrom-W2V2-100hrs-take-4-unfreeze-extractor-try-2
    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-4-unfreeze-extractor-try-2
  
  This model is a fine-tuned version of [rohitp1/Nystrom-W2V2-100hrs-take-4-unfreeze-extractor](https://huggingface.co/rohitp1/Nystrom-W2V2-100hrs-take-4-unfreeze-extractor) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 27.1915
  - Wer: 0.0869
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 23.1458       | 9.01  | 1000  | 28.9573         | 0.1039 |
  | 32.7156       | 18.02 | 2000  | 25.6155         | 0.1218 |
  | 43.506        | 27.03 | 3000  | 27.6332         | 0.1228 |
  | 43.3608       | 36.04 | 4000  | 26.0539         | 0.1169 |
  | 39.984        | 45.04 | 5000  | 25.9836         | 0.1137 |
  | 35.1977       | 54.05 | 6000  | 26.2060         | 0.1077 |
  | 30.1951       | 63.06 | 7000  | 27.0999         | 0.1033 |
  | 25.7519       | 72.07 | 8000  | 27.8459         | 0.0964 |
  | 22.1982       | 81.08 | 9000  | 27.9773         | 0.0908 |
  | 20.0551       | 90.09 | 10000 | 27.4222         | 0.0884 |
  | 19.4505       | 99.1  | 11000 | 27.1915         | 0.0869 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.11.0
  