readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - bleu
  model-index:
  - name: Vigec-V3
    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. -->
  
  # Vigec-V3
  
  This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2522
  - Bleu: 84.4788
  - Gen Len: 9.847
  
  ## 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: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 500
  - training_steps: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
  | 0.8764        | 0.0   | 500   | 0.6120          | 68.0114 | 8.4626  |
  | 0.6538        | 0.0   | 1000  | 0.4780          | 76.7403 | 10.015  |
  | 0.6234        | 0.01  | 1500  | 0.4207          | 78.1726 | 9.8394  |
  | 0.4513        | 0.01  | 2000  | 0.3845          | 79.1939 | 9.8914  |
  | 0.4153        | 0.01  | 2500  | 0.3580          | 80.171  | 9.7298  |
  | 0.5129        | 0.01  | 3000  | 0.3381          | 80.8668 | 9.8636  |
  | 0.5073        | 0.01  | 3500  | 0.3246          | 81.5543 | 9.81    |
  | 0.4623        | 0.01  | 4000  | 0.3106          | 82.1255 | 9.8684  |
  | 0.4444        | 0.02  | 4500  | 0.2973          | 82.5565 | 9.848   |
  | 0.4322        | 0.02  | 5000  | 0.2892          | 82.9623 | 9.872   |
  | 0.5029        | 0.02  | 5500  | 0.2803          | 83.3084 | 9.8648  |
  | 0.3686        | 0.02  | 6000  | 0.2765          | 83.4828 | 9.8602  |
  | 0.4123        | 0.02  | 6500  | 0.2693          | 83.7491 | 9.8432  |
  | 0.3593        | 0.03  | 7000  | 0.2674          | 83.8149 | 9.811   |
  | 0.3684        | 0.03  | 7500  | 0.2630          | 84.1745 | 9.8668  |
  | 0.3683        | 0.03  | 8000  | 0.2590          | 84.2294 | 9.8412  |
  | 0.3581        | 0.03  | 8500  | 0.2568          | 84.3428 | 9.8582  |
  | 0.3769        | 0.03  | 9000  | 0.2527          | 84.4367 | 9.8598  |
  | 0.4479        | 0.03  | 9500  | 0.2522          | 84.4749 | 9.847   |
  | 0.2856        | 0.04  | 10000 | 0.2522          | 84.4788 | 9.847   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  