readme: ---
  tags:
  - summarization
  - mT5_multilingual_XLSum
  - mt5
  - abstractive summarization
  - ar
  - xlsum
  - generated_from_trainer
  datasets:
  - xlsum
  model-index:
  - name: mT5_multilingual_XLSum-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_multilingual_XLSum-finetune-ar-xlsum
  
  This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the xlsum dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.2497
  - Rouge-1: 32.52
  - Rouge-2: 14.71
  - Rouge-l: 27.88
  - Gen Len: 41.45
  - Bertscore: 74.65
  
  ## 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: 8
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge-1 | Rouge-2 | Rouge-l | Gen Len | Bertscore |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 3.5465        | 1.0   | 585  | 3.3215          | 30.09   | 13.23   | 26.07   | 36.31   | 73.97     |
  | 3.3564        | 2.0   | 1170 | 3.2547          | 31.29   | 13.93   | 26.75   | 41.68   | 74.22     |
  | 3.2185        | 3.0   | 1755 | 3.2421          | 31.78   | 14.1    | 27.07   | 41.64   | 74.4      |
  | 3.1145        | 4.0   | 2340 | 3.2241          | 31.98   | 14.38   | 27.51   | 40.29   | 74.46     |
  | 3.031         | 5.0   | 2925 | 3.2313          | 32.3    | 14.67   | 27.83   | 39.81   | 74.61     |
  | 2.9627        | 6.0   | 3510 | 3.2348          | 32.39   | 14.65   | 27.76   | 40.02   | 74.6      |
  | 2.9088        | 7.0   | 4095 | 3.2439          | 32.5    | 14.66   | 27.81   | 41.2    | 74.65     |
  | 2.8649        | 8.0   | 4680 | 3.2497          | 32.52   | 14.71   | 27.88   | 41.45   | 74.65     |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  