readme: ---
  tags:
  - summarization
  - Arat5-base
  - abstractive summarization
  - ar
  - xlsum
  - generated_from_trainer
  datasets:
  - xlsum
  model-index:
  - name: AraT5-base-title-generation-finetune-ar-xlsum
    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-title-generation-finetune-ar-xlsum
  
  This model is a fine-tuned version of [UBC-NLP/AraT5-base-title-generation](https://huggingface.co/UBC-NLP/AraT5-base-title-generation) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.2837
  - Rouge-1: 32.46
  - Rouge-2: 15.15
  - Rouge-l: 28.38
  - Gen Len: 18.48
  - Bertscore: 74.24
  
  ## 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: 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: 10
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 5.815         | 1.0   | 293  | 4.7437          | 27.05   | 10.49   | 23.56   | 18.03   | 72.56     |
  | 5.0818        | 2.0   | 586  | 4.5004          | 28.92   | 11.97   | 25.09   | 18.61   | 73.08     |
  | 4.7855        | 3.0   | 879  | 4.3910          | 29.66   | 12.57   | 25.79   | 18.58   | 73.3      |
  | 4.588         | 4.0   | 1172 | 4.3469          | 30.22   | 13.05   | 26.36   | 18.59   | 73.61     |
  | 4.4388        | 5.0   | 1465 | 4.3226          | 30.88   | 13.81   | 27.01   | 18.65   | 73.78     |
  | 4.3162        | 6.0   | 1758 | 4.2990          | 30.9    | 13.6    | 26.92   | 18.68   | 73.78     |
  | 4.2178        | 7.0   | 2051 | 4.2869          | 31.35   | 14.01   | 27.41   | 18.57   | 73.96     |
  | 4.1387        | 8.0   | 2344 | 4.2794          | 31.28   | 13.98   | 27.34   | 18.6    | 73.87     |
  | 4.0787        | 9.0   | 2637 | 4.2806          | 31.45   | 14.17   | 27.46   | 18.66   | 73.97     |
  | 4.0371        | 10.0  | 2930 | 4.2837          | 31.55   | 14.19   | 27.52   | 18.65   | 74.0      |
  
  
  ### Framework versions
  
  - Transformers 4.20.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  