readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - bleu
  model-index:
  - name: Vigec-V5
    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-V5
  
  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: 0.3694
  - Bleu: 77.0736
  - Gen Len: 10.0475
  
  ## 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
  | 1.195         | 0.01  | 500   | 0.9492          | 43.0845 | 7.2405  |
  | 0.978         | 0.01  | 1000  | 0.7804          | 61.0671 | 9.7255  |
  | 0.8418        | 0.02  | 1500  | 0.6798          | 64.3811 | 9.9025  |
  | 0.8148        | 0.03  | 2000  | 0.6046          | 66.1944 | 10.043  |
  | 0.7622        | 0.04  | 2500  | 0.5513          | 68.2851 | 10.1215 |
  | 0.7199        | 0.04  | 3000  | 0.5146          | 69.7161 | 10.0795 |
  | 0.7898        | 0.05  | 3500  | 0.4869          | 71.1868 | 10.079  |
  | 0.6921        | 0.06  | 4000  | 0.4648          | 72.4203 | 10.0345 |
  | 0.6827        | 0.07  | 4500  | 0.4490          | 73.2133 | 10.039  |
  | 0.6102        | 0.07  | 5000  | 0.4355          | 73.6841 | 10.078  |
  | 0.5805        | 0.08  | 5500  | 0.4176          | 74.2559 | 10.059  |
  | 0.6806        | 0.09  | 6000  | 0.4081          | 74.7389 | 10.0655 |
  | 0.6544        | 0.09  | 6500  | 0.3958          | 75.2603 | 10.025  |
  | 0.6244        | 0.1   | 7000  | 0.3904          | 75.9306 | 10.0565 |
  | 0.7212        | 0.11  | 7500  | 0.3822          | 76.3268 | 10.0505 |
  | 0.5446        | 0.12  | 8000  | 0.3785          | 76.5306 | 10.0505 |
  | 0.5574        | 0.12  | 8500  | 0.3741          | 76.7101 | 10.0545 |
  | 0.6265        | 0.13  | 9000  | 0.3721          | 76.8858 | 10.043  |
  | 0.5379        | 0.14  | 9500  | 0.3695          | 77.001  | 10.051  |
  | 0.6164        | 0.14  | 10000 | 0.3694          | 77.0736 | 10.0475 |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  