readme: ---
  license: cc-by-nc-4.0
  tags:
  - generated_from_trainer
  metrics:
  - bleu
  model-index:
  - name: mbart-large-50-many-to-many-mmt-finetuned-fij_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. -->
  
  # mbart-large-50-many-to-many-mmt-finetuned-fij_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: 0.9598
  - Bleu: 45.0972
  - Gen Len: 42.752
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - gradient_accumulation_steps: 6
  - total_train_batch_size: 12
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 8000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
  | 2.4054        | 0.49  | 500  | 1.7028          | 24.9597 | 43.04   |
  | 1.6855        | 0.98  | 1000 | 1.3701          | 33.3128 | 42.2    |
  | 1.4042        | 1.47  | 1500 | 1.2224          | 37.6016 | 43.536  |
  | 1.2991        | 1.96  | 2000 | 1.1467          | 40.3541 | 42.428  |
  | 1.1819        | 2.45  | 2500 | 1.0950          | 42.2106 | 42.58   |
  | 1.1323        | 2.94  | 3000 | 1.0523          | 42.9418 | 42.76   |
  | 1.0676        | 3.43  | 3500 | 1.0238          | 43.4974 | 42.684  |
  | 1.0404        | 3.93  | 4000 | 1.0082          | 43.6092 | 42.616  |
  | 0.9882        | 4.42  | 4500 | 0.9942          | 44.7199 | 42.912  |
  | 0.982         | 4.91  | 5000 | 0.9814          | 44.8061 | 42.516  |
  | 0.9372        | 5.4   | 5500 | 0.9781          | 44.3808 | 42.476  |
  | 0.9382        | 5.89  | 6000 | 0.9675          | 45.0267 | 42.76   |
  | 0.915         | 6.38  | 6500 | 0.9659          | 45.0073 | 42.676  |
  | 0.9126        | 6.87  | 7000 | 0.9617          | 44.9582 | 42.548  |
  | 0.8903        | 7.36  | 7500 | 0.9609          | 44.8713 | 42.724  |
  | 0.8873        | 7.85  | 8000 | 0.9598          | 45.0972 | 42.752  |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  