readme: ---
  tags:
  - summarization
  - ar
  - encoder-decoder
  - arabert
  - arabert2arabert
  - Abstractive Summarization
  - generated_from_trainer
  datasets:
  - wiki_lingua
  model-index:
  - name: arabert2arabert-finetuned-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. -->
  
  # arabert2arabert-finetuned-ar-wikilingua
  
  This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.6877
  - Rouge-1: 13.2
  - Rouge-2: 3.43
  - Rouge-l: 12.45
  - Gen Len: 20.0
  - Bertscore: 64.88
  
  ## 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: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 250
  - num_epochs: 8
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 6.7667        | 1.0   | 156  | 5.3846          | 3.36    | 0.56    | 3.27    | 20.0    | 60.6      |
  | 5.257         | 2.0   | 312  | 5.0424          | 5.44    | 0.88    | 5.35    | 20.0    | 60.56     |
  | 4.743         | 3.0   | 468  | 4.8294          | 9.21    | 1.8     | 8.93    | 20.0    | 62.91     |
  | 4.3832        | 4.0   | 624  | 4.7240          | 9.88    | 2.19    | 9.6     | 20.0    | 62.65     |
  | 4.1166        | 5.0   | 780  | 4.6861          | 11.61   | 2.86    | 11.13   | 20.0    | 63.71     |
  | 3.91          | 6.0   | 936  | 4.6692          | 12.27   | 3.11    | 11.76   | 20.0    | 64.07     |
  | 3.7569        | 7.0   | 1092 | 4.6805          | 12.93   | 3.38    | 12.28   | 20.0    | 64.61     |
  | 3.6454        | 8.0   | 1248 | 4.6877          | 13.2    | 3.43    | 12.45   | 20.0    | 64.88     |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  