readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: Nystrom-W2V2-100hrs-take-4-unfreeze-extractor
    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
  
  This model is a fine-tuned version of [rohitp1/Nystrom-W2V2-100hrs-take-3](https://huggingface.co/rohitp1/Nystrom-W2V2-100hrs-take-3) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 27.1839
  - Wer: 0.0915
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|
  | 30.9159       | 9.01  | 1000  | 27.2422         | 0.1205 |
  | 47.7101       | 18.02 | 2000  | 25.0538         | 0.1366 |
  | 61.5118       | 27.03 | 3000  | 25.3063         | 0.1343 |
  | 57.2966       | 36.04 | 4000  | 25.0429         | 0.1276 |
  | 50.7161       | 45.04 | 5000  | 27.2507         | 0.1235 |
  | 43.7605       | 54.05 | 6000  | 25.9086         | 0.1145 |
  | 36.8698       | 63.06 | 7000  | 26.4890         | 0.1085 |
  | 31.0921       | 72.07 | 8000  | 26.9372         | 0.1021 |
  | 26.4249       | 81.08 | 9000  | 27.8031         | 0.0961 |
  | 23.336        | 90.09 | 10000 | 27.2129         | 0.0928 |
  | 22.2249       | 99.1  | 11000 | 27.1839         | 0.0915 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1
  - Datasets 2.7.1
  - Tokenizers 0.11.0
  