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# pko-t5-small-finetuned-paper-4564652

This model is a fine-tuned version of [paust/pko-t5-small](https://huggingface.co/paust/pko-t5-small) on the aihub_paper_summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4922
- Rouge1: 4.874
- Rouge2: 1.0497
- Rougel: 4.8599
- Rougelsum: 4.854
- Gen Len: 18.9953

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2

{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "datasets": ["aihub_paper_summarization"], "metrics": ["rouge"], "model-index": [{"name": "pko-t5-small-finetuned-paper-4564652", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "aihub_paper_summarization", "type": "aihub_paper_summarization", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "rouge", "value": 4.874, "name": "Rouge1"}]}]}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:aihub_paper_summarization license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-aihub_paper_summarization #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 




# pko-t5-small-finetuned-paper-4564652

This model is a fine-tuned version of paust/pko-t5-small on the aihub_paper_summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4922
- Rouge1: 4.874
- Rouge2: 1.0497
- Rougel: 4.8599
- Rougelsum: 4.854
- Gen Len: 18.9953

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2

# pko-t5-small-finetuned-paper-4564652

This model is a fine-tuned version of paust/pko-t5-small on the aihub_paper_summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4922
- Rouge1: 4.874
- Rouge2: 1.0497
- Rougel: 4.8599
- Rougelsum: 4.854
- Gen Len: 18.9953 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-aihub_paper_summarization #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # pko-t5-small-finetuned-paper-4564652

This model is a fine-tuned version of paust/pko-t5-small on the aihub_paper_summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4922
- Rouge1: 4.874
- Rouge2: 1.0497
- Rougel: 4.8599
- Rougelsum: 4.854
- Gen Len: 18.9953 ## Model description

More information needed ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Training procedure ### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP ### Training results ### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-aihub_paper_summarization #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# pko-t5-small-finetuned-paper-4564652

This model is a fine-tuned version of paust/pko-t5-small on the aihub_paper_summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4922
- Rouge1: 4.874
- Rouge2: 1.0497
- Rougel: 4.8599
- Rougelsum: 4.854
- Gen Len: 18.9953## Model description

More information needed## Intended uses & limitations

More information needed## Training and evaluation data

More information needed## Training procedure### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP### Training results### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2