readme: ---
  license: apache-2.0
  tags:
  - summarization
  - arabic
  - am
  - es
  - amharic
  - mt5
  - Abstractive Summarization
  - generated_from_trainer
  model-index:
  - name: mt5-base-finetuned-ar-sp
    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-ar-sp
  
  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: 3.2772
  - Rouge-1: 23.01
  - Rouge-2: 10.41
  - Rouge-l: 20.94
  - Gen Len: 19.0
  - Bertscore: 71.56
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 4.1968        | 1.0   | 1352 | 3.5142          | 18.69   | 6.73    | 16.97   | 19.0    | 70.3      |
  | 3.6932        | 2.0   | 2704 | 3.3799          | 20.67   | 8.38    | 18.75   | 19.0    | 70.82     |
  | 3.5058        | 3.0   | 4056 | 3.3184          | 20.97   | 8.58    | 19.08   | 19.0    | 71.08     |
  | 3.3832        | 4.0   | 5408 | 3.2851          | 21.59   | 8.94    | 19.63   | 19.0    | 71.28     |
  | 3.2994        | 5.0   | 6760 | 3.2772          | 21.84   | 9.23    | 19.85   | 19.0    | 71.34     |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  