readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - mlsum
  metrics:
  - rouge
  model-index:
  - name: mbart-large-turkish-sum
    results:
    - task:
        name: Summarization
        type: summarization
      dataset:
        name: mlsum tu
        type: mlsum
        args: tu
      metrics:
      - name: Rouge1
        type: rouge
        value: 46.7011
  ---
  
  # [Mukayese: Turkish NLP Strikes Back](https://arxiv.org/abs/2203.01215)
  
  ## Summarization: mukayese/mbart-large-turkish-sum
  
  This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on the mlsum/tu dataset.
  
  It achieves the following results on the evaluation set:
  
  - Rouge1: 46.7011
  - Rouge2: 34.0087
  - Rougel: 41.5475
  - Rougelsum: 43.2108
  
  Check [this](https://arxiv.org/abs/2203.01215) paper for more details on the model and the dataset.
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 2
  - eval_batch_size: 4
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - total_eval_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  - label_smoothing_factor: 0.1
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.8.2+cu111
  - Datasets 1.14.0
  - Tokenizers 0.10.3
  
  ### Citation
  
  ```
  @misc{safaya-etal-2022-mukayese,
      title={Mukayese: Turkish NLP Strikes Back},
      author={Ali Safaya and Emirhan Kurtuluş and Arda Göktoğan and Deniz Yuret},
      year={2022},
      eprint={2203.01215},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
  }
  ```
  