readme: ---
  license: cc-by-nc-4.0
  tags:
  - generated_from_trainer
  metrics:
  - bleu
  model-index:
  - name: NLLB-600m-vie_Latn-to-eng_Latn
    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. -->
  
  # NLLB-600m-vie_Latn-to-eng_Latn
  
  This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.1189
  - Bleu: 36.6767
  - Gen Len: 47.504
  
  ## 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: 3
  - eval_batch_size: 3
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 24
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 10000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
  | 1.9294        | 2.24  | 1000  | 1.5970          | 23.6201 | 48.1    |
  | 1.4           | 4.47  | 2000  | 1.3216          | 28.9526 | 45.156  |
  | 1.2071        | 6.71  | 3000  | 1.2245          | 32.5538 | 46.576  |
  | 1.0893        | 8.95  | 4000  | 1.1720          | 34.265  | 46.052  |
  | 1.0064        | 11.19 | 5000  | 1.1497          | 34.9249 | 46.508  |
  | 0.9562        | 13.42 | 6000  | 1.1331          | 36.4619 | 47.244  |
  | 0.9183        | 15.66 | 7000  | 1.1247          | 36.4723 | 47.26   |
  | 0.8858        | 17.9  | 8000  | 1.1198          | 36.7058 | 47.376  |
  | 0.8651        | 20.13 | 9000  | 1.1201          | 36.7897 | 47.496  |
  | 0.8546        | 22.37 | 10000 | 1.1189          | 36.6767 | 47.504  |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  