

# [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 [google/mt5-base](https://huggingface.co/google/mt5-base) on the mlsum/tu dataset.

It achieves the following results on the evaluation set:

- Rouge1: 47.4222
- Rouge2: 34.8624
- Rougel: 42.2487
- Rougelsum: 43.9494

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: 0.0005
- 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
- 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}
}
```

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge"], "base_model": "google/mt5-base", "model-index": [{"name": "mt5-base-turkish-sum", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "mlsum tu", "type": "mlsum", "args": "tu"}, "metrics": [{"type": "rouge", "value": 47.4222, "name": "Rouge1"}]}]}]}
transformers pytorch safetensors mt5 text2text-generation generated_from_trainer dataset:mlsum arxiv:2203.01215 base_model:google/mt5-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #arxiv-2203.01215 #base_model-google/mt5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 



# Mukayese: Turkish NLP Strikes Back

## Summarization: mukayese/mbart-large-turkish-sum

This model is a fine-tuned version of google/mt5-base on the mlsum/tu dataset.

It achieves the following results on the evaluation set:

- Rouge1: 47.4222
- Rouge2: 34.8624
- Rougel: 42.2487
- Rougelsum: 43.9494

Check this paper for more details on the model and the dataset.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- 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
- 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/mbart-large-turkish-sum

This model is a fine-tuned version of google/mt5-base on the mlsum/tu dataset.

It achieves the following results on the evaluation set:

- Rouge1: 47.4222
- Rouge2: 34.8624
- Rougel: 42.2487
- Rougelsum: 43.9494

Check this paper for more details on the model and the dataset. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- 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
- 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 #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #arxiv-2203.01215 #base_model-google/mt5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # Mukayese: Turkish NLP Strikes Back ## Summarization: mukayese/mbart-large-turkish-sum

This model is a fine-tuned version of google/mt5-base on the mlsum/tu dataset.

It achieves the following results on the evaluation set:

- Rouge1: 47.4222
- Rouge2: 34.8624
- Rougel: 42.2487
- Rougelsum: 43.9494

Check this paper for more details on the model and the dataset. ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- 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
- 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 #mt5 #text2text-generation #generated_from_trainer #dataset-mlsum #arxiv-2203.01215 #base_model-google/mt5-base #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# Mukayese: Turkish NLP Strikes Back## Summarization: mukayese/mbart-large-turkish-sum

This model is a fine-tuned version of google/mt5-base on the mlsum/tu dataset.

It achieves the following results on the evaluation set:

- Rouge1: 47.4222
- Rouge2: 34.8624
- Rougel: 42.2487
- Rougelsum: 43.9494

Check this paper for more details on the model and the dataset.### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- 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
- label_smoothing_factor: 0.1### Framework versions

- Transformers 4.11.3
- Pytorch 1.8.2+cu111
- Datasets 1.14.0
- Tokenizers 0.10.3