readme: ---
  license: apache-2.0
  tags:
  - summarization
  - generated_from_trainer
  datasets:
  - xlsum
  metrics:
  - rouge
  model-index:
  - name: mt5-finetuned-en-ar
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: xlsum
        type: xlsum
        args: arabic
      metrics:
      - name: Rouge1
        type: rouge
        value: 0.2824
  ---
  
  <!-- 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. -->
  
  # mt5-finetuned-en-ar
  
  This model is a fine-tuned version of [ahmeddbahaa/mt5-small-finetuned-mt5-en](https://huggingface.co/ahmeddbahaa/mt5-small-finetuned-mt5-en) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.2314
  - Rouge1: 0.2824
  - Rouge2: 0.0
  - Rougel: 0.2902
  - Rougelsum: 0.298
  
  ## 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: 0.0005
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
  | 3.1685        | 1.0   | 4130  | 2.4262          | 0.0941 | 0.0235 | 0.1098 | 0.1098    |
  | 2.686         | 2.0   | 8260  | 2.2853          | 0.2824 | 0.0    | 0.298  | 0.298     |
  | 2.481         | 3.0   | 12390 | 2.2314          | 0.2824 | 0.0    | 0.2902 | 0.298     |
  
  
  ### Framework versions
  
  - Transformers 4.17.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  