readme: ---
  license: apache-2.0
  tags:
  - summarization
  - mT5_multilingual_XLSum
  - mt5
  - abstractive summarization
  - ar
  - xlsum
  - generated_from_trainer
  datasets:
  - xlsum
  model-index:
  - name: mt5-base-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. -->
  
  # mt5-base-finetune-ar-xlsum
  
  This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.2546
  - Rouge-1: 22.2
  - Rouge-2: 9.57
  - Rouge-l: 20.26
  - Gen Len: 19.0
  - Bertscore: 71.43
  
  ## 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: 16
  - total_train_batch_size: 64
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 4.9261        | 1.0   | 585  | 3.6314          | 18.19   | 6.49    | 16.37   | 19.0    | 70.17     |
  | 3.8429        | 2.0   | 1170 | 3.4253          | 19.45   | 7.58    | 17.73   | 19.0    | 70.35     |
  | 3.6311        | 3.0   | 1755 | 3.3569          | 20.83   | 8.54    | 18.9    | 19.0    | 70.89     |
  | 3.4917        | 4.0   | 2340 | 3.3101          | 20.77   | 8.53    | 18.89   | 19.0    | 70.98     |
  | 3.3873        | 5.0   | 2925 | 3.2867          | 21.47   | 9.0     | 19.54   | 19.0    | 71.23     |
  | 3.3037        | 6.0   | 3510 | 3.2693          | 21.41   | 9.0     | 19.5    | 19.0    | 71.21     |
  | 3.2357        | 7.0   | 4095 | 3.2581          | 22.05   | 9.36    | 20.04   | 19.0    | 71.43     |
  | 3.1798        | 8.0   | 4680 | 3.2522          | 22.21   | 9.56    | 20.23   | 19.0    | 71.41     |
  | 3.1359        | 9.0   | 5265 | 3.2546          | 22.27   | 9.58    | 20.23   | 19.0    | 71.46     |
  | 3.0997        | 10.0  | 5850 | 3.2546          | 22.2    | 9.57    | 20.26   | 19.0    | 71.43     |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  