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Mukayese: Turkish NLP Strikes Back

Summarization: mukayese/transformer-turkish-summarization

This model is uncased, it was initialized from scratch and trained only the mlsum/tu dataset with no pre-training.

It achieves the following results on the evaluation set:

  • Rouge1: 43.2049
  • Rouge2: 30.7082
  • Rougel: 38.1981
  • Rougelsum: 39.9453

Check this paper for more details on the model and the dataset.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15.0
  • 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}
}
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Dataset used to train mukayese/transformer-turkish-summarization

Evaluation results