readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - wer
  model-index:
  - name: CV30_finetuning
    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. -->
  
  # CV30_finetuning
  
  This model is a fine-tuned version of [Roshana/CV11_finetuning1](https://huggingface.co/Roshana/CV11_finetuning1) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7956
  - Wer: 0.3677
  - Cer: 0.1436
  
  ## 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: 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
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
  | 0.1203        | 0.86  | 400  | 0.7747          | 0.4192 | 0.1656 |
  | 0.2191        | 1.72  | 800  | 0.7546          | 0.4356 | 0.1771 |
  | 0.213         | 2.59  | 1200 | 0.6965          | 0.4276 | 0.1709 |
  | 0.1858        | 3.45  | 1600 | 0.7189          | 0.4156 | 0.1673 |
  | 0.1597        | 4.31  | 2000 | 0.7462          | 0.4031 | 0.1606 |
  | 0.1398        | 5.17  | 2400 | 0.7622          | 0.3944 | 0.1584 |
  | 0.1245        | 6.03  | 2800 | 0.7527          | 0.3917 | 0.1553 |
  | 0.1035        | 6.9   | 3200 | 0.7347          | 0.3819 | 0.1532 |
  | 0.0918        | 7.76  | 3600 | 0.7825          | 0.3786 | 0.1497 |
  | 0.0767        | 8.62  | 4000 | 0.7909          | 0.3724 | 0.1444 |
  | 0.0722        | 9.48  | 4400 | 0.7956          | 0.3677 | 0.1436 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  