README.md

---
tags:
- generated_from_trainer
datasets:
- mlsum
metrics:
- rouge
base_model: facebook/mbart-large-50
model-index:
- name: mbart-large-turkish-sum
  results:
  - task:
      type: summarization
      name: Summarization
    dataset:
      name: mlsum tu
      type: mlsum
      args: tu
    metrics:
    - type: rouge
      value: 46.7011
      name: Rouge1
---

# [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}
}
```