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mukayese_-_transformer-turkish-summarization-4bits

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mukayese_-_transformer-turkish-summarization-4bits
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transformer-turkish-summarization - bnb 4bits
- Model creator: https://huggingface.co/mukayese/
- Original model: https://huggingface.co/mukayese/transformer-turkish-summarization/




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](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}
}
```