readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - opus100
  metrics:
  - bleu
  model-index:
  - name: mbart-large-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: opus100
        type: opus100
        args: ar-en
      metrics:
      - name: Bleu
        type: bleu
        value: 10.5645
  ---
  
  <!-- 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-cc25-en-ar-evaluated-en-to-ar-1000instancesopus-leaningRate2e-05-batchSize2
  
  This model is a fine-tuned version of [akhooli/mbart-large-cc25-en-ar](https://huggingface.co/akhooli/mbart-large-cc25-en-ar) on the opus100 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4673
  - Bleu: 10.5645
  - Meteor: 0.0783
  - Gen Len: 10.23
  
  ## 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
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 11
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Bleu    | Meteor | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
  | 8.1731        | 0.25  | 100  | 2.8417          | 0.9599  | 0.028  | 230.885 |
  | 0.6743        | 0.5   | 200  | 0.4726          | 6.4055  | 0.0692 | 14.81   |
  | 0.3028        | 0.75  | 300  | 0.4572          | 6.7544  | 0.0822 | 23.92   |
  | 0.2555        | 1.0   | 400  | 0.4172          | 8.4078  | 0.0742 | 13.655  |
  | 0.1644        | 1.25  | 500  | 0.4236          | 9.284   | 0.071  | 13.03   |
  | 0.1916        | 1.5   | 600  | 0.4222          | 4.8976  | 0.0779 | 32.225  |
  | 0.2011        | 1.75  | 700  | 0.4305          | 7.6909  | 0.0738 | 16.675  |
  | 0.1612        | 2.0   | 800  | 0.4416          | 10.8622 | 0.0855 | 10.91   |
  | 0.116         | 2.25  | 900  | 0.4673          | 10.5645 | 0.0783 | 10.23   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  