readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - mt_eng_vietnamese
  metrics:
  - bleu
  model-index:
  - name: t5vi-finetuned-en-to-vi
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: mt_eng_vietnamese
        type: mt_eng_vietnamese
        args: iwslt2015-en-vi
      metrics:
      - name: Bleu
        type: bleu
        value: 13.547
  ---
  
  <!-- 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. -->
  
  # t5vi-finetuned-en-to-vi
  
  This model is a fine-tuned version of [imthanhlv/t5vi](https://huggingface.co/imthanhlv/t5vi) on the mt_eng_vietnamese dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.3827
  - Bleu: 13.547
  - Gen Len: 17.3719
  
  ## 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: 2e-05
  - train_batch_size: 20
  - eval_batch_size: 20
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
  | 1.8026        | 1.0   | 6666  | 1.5907          | 10.9756 | 17.3231 |
  | 1.6217        | 2.0   | 13332 | 1.4635          | 12.375  | 17.3444 |
  | 1.5087        | 3.0   | 19998 | 1.4131          | 13.1828 | 17.3924 |
  | 1.4446        | 4.0   | 26664 | 1.3915          | 13.5217 | 17.3617 |
  | 1.4076        | 5.0   | 33330 | 1.3827          | 13.547  | 17.3719 |
  
  
  ### Framework versions
  
  - Transformers 4.19.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  