readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: pseudolabeling-step2-F01-Pass-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. -->
  
  # pseudolabeling-step2-F01-Pass-2
  
  This model is a fine-tuned version of [monideep2255/XLRS-torgo](https://huggingface.co/monideep2255/XLRS-torgo) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2704
  - Wer: 1.1942
  
  ## 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: 0.0003
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|
  | 1.9509        | 2.94  | 400  | 1.1949          | 1.2734 |
  | 0.5686        | 5.88  | 800  | 1.0452          | 1.2266 |
  | 0.4179        | 8.82  | 1200 | 1.1876          | 1.2032 |
  | 0.3137        | 11.76 | 1600 | 1.2691          | 1.2572 |
  | 0.2329        | 14.7  | 2000 | 1.2944          | 1.2104 |
  | 0.1851        | 17.64 | 2400 | 1.4389          | 1.2626 |
  | 0.1427        | 20.59 | 2800 | 1.3325          | 1.2608 |
  | 0.1101        | 23.53 | 3200 | 1.4132          | 1.2176 |
  | 0.0805        | 26.47 | 3600 | 1.3443          | 1.2482 |
  | 0.0645        | 29.41 | 4000 | 1.2704          | 1.1942 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 1.18.3
  - Tokenizers 0.13.2
  