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