
# mT5-Xl Turkish Summarization

This model is a fine-tuned version of [google/mt5-xl](https://huggingface.co/google/mt5-xl) on the musabg/wikipedia-tr-summarization dataset.

This can be used with HF summarization pipeline.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Eval results

It achieves the following results on the evaluation set:
- Loss: 0.5676
- Rouge1: 56.4468
- Rouge2: 41.3258
- Rougel: 48.1909
- Rougelsum: 48.4284
- Gen Len: 75.9265

### Training results



### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["tr"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["musabg/wikipedia-tr-summarization"], "metrics": ["rouge"], "model-index": [{"name": "mt5-xl-tr-summarization", "results": [{"task": {"type": "summarization", "name": "Summarization"}, "dataset": {"name": "musabg/wikipedia-tr-summarization", "type": "musabg/wikipedia-tr-summarization", "split": "validation"}, "metrics": [{"type": "rouge", "value": 56.4468, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard mt5 text2text-generation generated_from_trainer tr dataset:musabg/wikipedia-tr-summarization license:apache-2.0 model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #tr #dataset-musabg/wikipedia-tr-summarization #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


# mT5-Xl Turkish Summarization

This model is a fine-tuned version of google/mt5-xl on the musabg/wikipedia-tr-summarization dataset.

This can be used with HF summarization pipeline.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Eval results

It achieves the following results on the evaluation set:
- Loss: 0.5676
- Rouge1: 56.4468
- Rouge2: 41.3258
- Rougel: 48.1909
- Rougelsum: 48.4284
- Gen Len: 75.9265

### Training results



### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
# mT5-Xl Turkish Summarization

This model is a fine-tuned version of google/mt5-xl on the musabg/wikipedia-tr-summarization dataset.

This can be used with HF summarization pipeline. ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0 ### Eval results

It achieves the following results on the evaluation set:
- Loss: 0.5676
- Rouge1: 56.4468
- Rouge2: 41.3258
- Rougel: 48.1909
- Rougelsum: 48.4284
- Gen Len: 75.9265 ### Training results ### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #mt5 #text2text-generation #generated_from_trainer #tr #dataset-musabg/wikipedia-tr-summarization #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # mT5-Xl Turkish Summarization

This model is a fine-tuned version of google/mt5-xl on the musabg/wikipedia-tr-summarization dataset.

This can be used with HF summarization pipeline. ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0 ### Eval results

It achieves the following results on the evaluation set:
- Loss: 0.5676
- Rouge1: 56.4468
- Rouge2: 41.3258
- Rougel: 48.1909
- Rougelsum: 48.4284
- Gen Len: 75.9265 ### Training results ### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3