readme: ---
  tags:
  - summarization
  - persian
  - MBart50
  - Abstractive Summarization
  - generated_from_trainer
  datasets:
  - xlsum
  model-index:
  - name: mbart-large-50-finetuned-persian
    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. -->
  
  # mbart-large-50-finetuned-persian
  
  This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.1932
  - Rouge-1: 26.11
  - Rouge-2: 8.11
  - Rouge-l: 21.09
  - Gen Len: 37.29
  - Bertscore: 71.08
  
  ## 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: 8
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 5.5612        | 1.0   | 1476 | 4.5015          | 17.07   | 3.14    | 13.54   | 47.49   | 66.83     |
  | 4.3049        | 2.0   | 2952 | 4.1055          | 22.63   | 5.89    | 18.03   | 40.43   | 69.23     |
  | 3.8154        | 3.0   | 4428 | 3.9822          | 24.57   | 7.15    | 19.74   | 37.35   | 70.36     |
  | 3.3401        | 4.0   | 5904 | 4.0088          | 25.84   | 7.96    | 20.95   | 37.56   | 70.83     |
  | 2.8879        | 5.0   | 7380 | 4.1932          | 26.24   | 8.26    | 21.23   | 37.78   | 71.05     |
  
  
  ### Framework versions
  
  - Transformers 4.19.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.1
  - Tokenizers 0.12.1
  