readme: ---
  tags:
  - summarization
  - ar
  - encoder-decoder
  - roberta
  - xlmroberta2xlmroberta
  - Abstractive Summarization
  - generated_from_trainer
  datasets:
  - wiki_lingua
  model-index:
  - name: xlmroberta2xlmroberta-finetuned-ar-wikilingua
    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. -->
  
  # xlmroberta2xlmroberta-finetuned-ar-wikilingua
  
  This model is a fine-tuned version of [](https://huggingface.co/) on the wiki_lingua dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.7757
  - Rouge-1: 11.2
  - Rouge-2: 1.96
  - Rouge-l: 10.28
  - Gen Len: 19.8
  - Bertscore: 66.27
  
  ## 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: 5e-05
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|
  | 8.03          | 1.0   | 312  | 7.3208          | 0.19    | 0.0     | 0.19    | 20.0    | 54.84     |
  | 7.2309        | 2.0   | 624  | 7.1107          | 1.17    | 0.03    | 1.16    | 20.0    | 60.0      |
  | 7.0752        | 3.0   | 936  | 7.0061          | 2.58    | 0.15    | 2.55    | 20.0    | 63.52     |
  | 6.7538        | 4.0   | 1248 | 6.4189          | 5.75    | 0.46    | 5.55    | 19.95   | 62.83     |
  | 6.1513        | 5.0   | 1560 | 5.8402          | 8.46    | 1.04    | 8.08    | 19.2    | 64.25     |
  | 5.6639        | 6.0   | 1872 | 5.3938          | 8.62    | 1.17    | 8.16    | 19.28   | 64.81     |
  | 5.2857        | 7.0   | 2184 | 5.0719          | 9.34    | 1.41    | 8.61    | 19.71   | 65.29     |
  | 5.027         | 8.0   | 2496 | 4.9047          | 10.42   | 1.52    | 9.57    | 19.57   | 65.75     |
  | 4.8747        | 9.0   | 2808 | 4.8032          | 10.79   | 1.71    | 9.91    | 19.42   | 66.2      |
  | 4.7855        | 10.0  | 3120 | 4.7757          | 11.01   | 1.73    | 10.04   | 19.55   | 66.24     |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  