readme: ---
  tags:
  - summarization
  - ar
  - encoder-decoder
  - xlm-roberta
  - Abstractive Summarization
  - roberta
  - generated_from_trainer
  datasets:
  - xlsum
  model-index:
  - name: xlmroberta2xlmroberta-finetune-summarization-ar
    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. -->
  
  # xlmroberta2xlmroberta-finetune-summarization-ar
  
  This model is a fine-tuned version of [](https://huggingface.co/) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.1298
  - Rouge-1: 21.69
  - Rouge-2: 8.73
  - Rouge-l: 19.52
  - Gen Len: 19.96
  - Bertscore: 71.0
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 32
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 8.0645        | 1.0   | 1172  | 7.3567          | 8.22    | 0.66    | 7.94    | 20.0    | 58.18     |
  | 7.2042        | 2.0   | 2344  | 6.6058          | 12.12   | 2.19    | 11.4    | 20.0    | 63.24     |
  | 6.4168        | 3.0   | 3516  | 5.8784          | 16.46   | 4.31    | 15.15   | 20.0    | 66.3      |
  | 5.4622        | 4.0   | 4688  | 4.7931          | 17.6    | 5.87    | 15.9    | 19.99   | 69.21     |
  | 4.7829        | 5.0   | 5860  | 4.4418          | 19.17   | 6.74    | 17.22   | 19.98   | 70.23     |
  | 4.4829        | 6.0   | 7032  | 4.2950          | 19.8    | 7.11    | 17.74   | 19.98   | 70.38     |
  | 4.304         | 7.0   | 8204  | 4.2155          | 20.71   | 7.59    | 18.56   | 19.98   | 70.66     |
  | 4.1778        | 8.0   | 9376  | 4.1632          | 21.1    | 7.94    | 18.99   | 19.98   | 70.86     |
  | 4.0886        | 9.0   | 10548 | 4.1346          | 21.44   | 8.03    | 19.28   | 19.98   | 70.93     |
  | 4.0294        | 10.0  | 11720 | 4.1298          | 21.51   | 8.14    | 19.33   | 19.98   | 71.02     |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  