readme: ---
  license: apache-2.0
  tags:
  - summarization
  - arabic
  - ar
  - fa
  - persian
  - mt5
  - Abstractive Summarization
  - generated_from_trainer
  model-index:
  - name: mt5-base-finetuned-arfa
    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. -->
  
  # mt5-base-finetuned-arfa
  
  This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.1784
  - Rouge-1: 25.68
  - Rouge-2: 11.8
  - Rouge-l: 22.99
  - Gen Len: 18.99
  - Bertscore: 71.78
  
  ## 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: 4
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 3.9866        | 1.0   | 2649  | 3.3635          | 21.94   | 8.59    | 19.5    | 18.99   | 70.6      |
  | 3.5637        | 2.0   | 5298  | 3.2557          | 24.01   | 10.0    | 21.26   | 18.99   | 71.22     |
  | 3.4016        | 3.0   | 7947  | 3.2005          | 24.4    | 10.43   | 21.72   | 18.98   | 71.36     |
  | 3.2985        | 4.0   | 10596 | 3.1784          | 24.68   | 10.73   | 22.01   | 18.98   | 71.51     |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  