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---
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](https://arxiv.org/abs/2203.01215)
## 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](https://arxiv.org/abs/2203.01215) 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}
}
``` |