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# bart-v2-dialouge

This model is a fine-tuned version of [hyunwoongko/kobart](https://huggingface.co/hyunwoongko/kobart) on the naem1023/aihub-dialogue dataset.

## 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: 1e-05
- train_batch_size: 150
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0

### Training results



### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["naem1023/aihub-dialogue"], "model-index": [{"name": "bart-v2-dialouge", "results": []}]}
transformers pytorch bart text2text-generation generated_from_trainer dataset:naem1023/aihub-dialogue license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-naem1023/aihub-dialogue #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# bart-v2-dialouge

This model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-dialogue dataset.

## 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: 1e-05
- train_batch_size: 150
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0

### Training results



### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1

# bart-v2-dialouge

This model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-dialogue dataset. ## 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: 1e-05
- train_batch_size: 150
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0 ### Training results ### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-naem1023/aihub-dialogue #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # bart-v2-dialouge

This model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-dialogue dataset. ## 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: 1e-05
- train_batch_size: 150
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0 ### Training results ### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-naem1023/aihub-dialogue #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# bart-v2-dialouge

This model is a fine-tuned version of hyunwoongko/kobart on the naem1023/aihub-dialogue dataset.## 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: 1e-05
- train_batch_size: 150
- eval_batch_size: 40
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 1200
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6.0### Training results### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.4.0
- Tokenizers 0.12.1