readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - bleu
  model-index:
  - name: mbart-large-50-many-to-many-mmt-finetuned-acw-to-en
    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-ar-to-en
  
  This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5204
  - Bleu: 34.8213
  - Gen Len: 33.544
  
  ## 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: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - training_steps: 5000
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
  | 1.4657        | 1.0   | 816  | 1.1739          | 30.1212 | 32.868  |
  | 0.8541        | 2.0   | 1632 | 1.1190          | 33.0098 | 32.808  |
  | 0.6176        | 3.0   | 2448 | 1.1681          | 33.3634 | 32.756  |
  | 0.3397        | 4.0   | 3264 | 1.3327          | 33.2941 | 33.6    |
  | 0.2227        | 5.0   | 4080 | 1.4211          | 33.9298 | 33.128  |
  | 0.1597        | 6.0   | 4896 | 1.5157          | 34.7405 | 33.496  |
  | 0.1426        | 6.13  | 5000 | 1.5204          | 34.8213 | 33.544  |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  