
# [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}
}
```
{"language": ["tr"], "license": "mit", "datasets": ["mlsum"], "metrics": ["rouge"], "pipeline_tag": "summarization", "model-index": [{"name": "mukayese/transformer-turkish-summarization", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "mlsum tu", "type": "mlsum", "args": "tu"}, "metrics": [{"type": "rouge", "value": 43.2049, "name": "Rouge1"}]}]}]}
transformers pytorch safetensors bart text2text-generation summarization tr dataset:mlsum arxiv:2203.01215 license:mit model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #summarization #tr #dataset-mlsum #arxiv-2203.01215 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 


# 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


# 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
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #summarization #tr #dataset-mlsum #arxiv-2203.01215 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # 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
passage: TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #summarization #tr #dataset-mlsum #arxiv-2203.01215 #license-mit #model-index #autotrain_compatible #endpoints_compatible #region-us 
# 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