Original model description:

datasets: - mlsum metrics: - rouge model-index: - name: mukayese/transformer-turkish-summarization results: - task: name: Summarization type: summarization dataset: name: mlsum tu type: mlsum args: tu metrics: - name: Rouge1 type: rouge value: 43.2049 license: mit language: - tr pipeline_tag: summarization

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}
}