readme: ---
  tags:
  - summarization
  - ar
  - Abstractive Summarization
  - generated_from_trainer
  datasets:
  - wiki_lingua
  model-index:
  - name: AraT5-base-finetune-ar-wikilingua
    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. -->
  
  # AraT5-base-finetune-ar-wikilingua
  
  This model is a fine-tuned version of [UBC-NLP/AraT5-base](https://huggingface.co/UBC-NLP/AraT5-base) on the wiki_lingua dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.6110
  - Rouge-1: 19.97
  - Rouge-2: 6.9
  - Rouge-l: 18.25
  - Gen Len: 18.45
  - Bertscore: 69.44
  
  ## 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
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 250
  - num_epochs: 10
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 11.5412       | 1.0   | 312  | 6.8825          | 5.2     | 0.69    | 5.04    | 19.0    | 63.2      |
  | 6.5212        | 2.0   | 624  | 5.8992          | 8.89    | 1.4     | 8.36    | 17.92   | 63.9      |
  | 5.8302        | 3.0   | 936  | 5.3712          | 9.99    | 2.21    | 9.54    | 15.87   | 65.08     |
  | 5.406         | 4.0   | 1248 | 5.0632          | 13.94   | 3.5     | 13.0    | 15.95   | 66.83     |
  | 5.1109        | 5.0   | 1560 | 4.8718          | 15.28   | 4.34    | 14.27   | 18.26   | 66.83     |
  | 4.9004        | 6.0   | 1872 | 4.7631          | 16.65   | 4.92    | 15.46   | 17.73   | 67.75     |
  | 4.754         | 7.0   | 2184 | 4.6920          | 18.31   | 5.79    | 16.9    | 18.17   | 68.55     |
  | 4.6369        | 8.0   | 2496 | 4.6459          | 18.6    | 6.12    | 17.16   | 18.16   | 68.66     |
  | 4.5595        | 9.0   | 2808 | 4.6153          | 18.94   | 6.1     | 17.39   | 17.82   | 68.99     |
  | 4.4967        | 10.0  | 3120 | 4.6110          | 19.15   | 6.25    | 17.55   | 17.91   | 69.09     |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  