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# bart-large-mlsum

This model is a fine-tuned version of [Kafaite24/bart-large-mlsum](https://huggingface.co/Kafaite24/bart-large-mlsum) on the mlsum 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["mlsum"], "metrics": ["rouge", "bertscore"], "pipeline_tag": "summarization", "model-index": [{"name": "bart-large-mlsum", "results": []}]}
transformers pytorch bart text2text-generation generated_from_trainer summarization dataset:mlsum license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #summarization #dataset-mlsum #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# bart-large-mlsum

This model is a fine-tuned version of Kafaite24/bart-large-mlsum on the mlsum 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
# bart-large-mlsum

This model is a fine-tuned version of Kafaite24/bart-large-mlsum on the mlsum 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- label_smoothing_factor: 0.1 ### Training results ### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #summarization #dataset-mlsum #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # bart-large-mlsum

This model is a fine-tuned version of Kafaite24/bart-large-mlsum on the mlsum 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- label_smoothing_factor: 0.1 ### Training results ### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #summarization #dataset-mlsum #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# bart-large-mlsum

This model is a fine-tuned version of Kafaite24/bart-large-mlsum on the mlsum 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- label_smoothing_factor: 0.1### Training results### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2