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# BaSum

This model is a fine-tuned version of [digit82/kobart-summarization](https://huggingface.co/digit82/kobart-summarization) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5612
- eval_rouge1: 8.1463
- eval_rouge2: 1.662
- eval_rougeL: 8.0941
- eval_rougeLsum: 8.1522
- eval_gen_len: 30.0
- eval_runtime: 72.334
- eval_samples_per_second: 11.17
- eval_steps_per_second: 0.705
- epoch: 3.47
- step: 3500

## 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: 69
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "model-index": [{"name": "BaSum", "results": []}]}
transformers pytorch tensorboard bart text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# BaSum

This model is a fine-tuned version of digit82/kobart-summarization on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5612
- eval_rouge1: 8.1463
- eval_rouge2: 1.662
- eval_rougeL: 8.0941
- eval_rougeLsum: 8.1522
- eval_gen_len: 30.0
- eval_runtime: 72.334
- eval_samples_per_second: 11.17
- eval_steps_per_second: 0.705
- epoch: 3.47
- step: 3500

## 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: 69
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

# BaSum

This model is a fine-tuned version of digit82/kobart-summarization on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5612
- eval_rouge1: 8.1463
- eval_rouge2: 1.662
- eval_rougeL: 8.0941
- eval_rougeLsum: 8.1522
- eval_gen_len: 30.0
- eval_runtime: 72.334
- eval_samples_per_second: 11.17
- eval_steps_per_second: 0.705
- epoch: 3.47
- step: 3500 ## 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: 69
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20 ### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # BaSum

This model is a fine-tuned version of digit82/kobart-summarization on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5612
- eval_rouge1: 8.1463
- eval_rouge2: 1.662
- eval_rougeL: 8.0941
- eval_rougeLsum: 8.1522
- eval_gen_len: 30.0
- eval_runtime: 72.334
- eval_samples_per_second: 11.17
- eval_steps_per_second: 0.705
- epoch: 3.47
- step: 3500 ## 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: 69
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20 ### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bart #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# BaSum

This model is a fine-tuned version of digit82/kobart-summarization on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.5612
- eval_rouge1: 8.1463
- eval_rouge2: 1.662
- eval_rougeL: 8.0941
- eval_rougeLsum: 8.1522
- eval_gen_len: 30.0
- eval_runtime: 72.334
- eval_samples_per_second: 11.17
- eval_steps_per_second: 0.705
- epoch: 3.47
- step: 3500## 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: 69
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20### Framework versions

- Transformers 4.27.3
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2