readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - opus100
  metrics:
  - bleu
  model-index:
  - name: opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1
    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: 26.8232
  ---
  
  <!-- 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. -->
  
  # opus-mt-en-ar-evaluated-en-to-ar-4000instances-opus-leaningRate2e-05-batchSize8-11-action-1
  
  This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ar](https://huggingface.co/Helsinki-NLP/opus-mt-en-ar) on the opus100 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1717
  - Bleu: 26.8232
  - Meteor: 0.172
  - Gen Len: 12.1288
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 11
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Bleu    | Meteor | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|
  | 0.7364        | 0.25  | 100  | 0.1731          | 27.2753 | 0.1729 | 12.0887 |
  | 0.2175        | 0.5   | 200  | 0.1731          | 27.2055 | 0.1722 | 11.5675 |
  | 0.2193        | 0.75  | 300  | 0.1722          | 27.3277 | 0.1798 | 12.1325 |
  | 0.2321        | 1.0   | 400  | 0.1750          | 27.5152 | 0.1762 | 11.925  |
  | 0.1915        | 1.25  | 500  | 0.1690          | 27.5043 | 0.1751 | 11.9038 |
  | 0.1794        | 1.5   | 600  | 0.1719          | 26.8607 | 0.1713 | 11.8138 |
  | 0.1741        | 1.75  | 700  | 0.1725          | 26.974  | 0.1724 | 11.8462 |
  | 0.1732        | 2.0   | 800  | 0.1717          | 26.8232 | 0.172  | 12.1288 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  