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# bart-large-cnn-qmsum-meeting-summarization

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7578
- Rouge1: 37.9431
- Rouge2: 10.6366
- Rougel: 25.5782
- Rougelsum: 33.0209
- Gen Len: 72.7714

## 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: 8
- eval_batch_size: 8
- 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: 500
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["yawnick/QMSum"], "metrics": ["rouge"], "model-index": [{"name": "bart-large-cnn-qmsum-meeting-summarization", "results": []}]}
transformers pytorch bart text2text-generation generated_from_trainer dataset:yawnick/QMSum license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# bart-large-cnn-qmsum-meeting-summarization

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7578
- Rouge1: 37.9431
- Rouge2: 10.6366
- Rougel: 25.5782
- Rougelsum: 33.0209
- Gen Len: 72.7714

## 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: 8
- eval_batch_size: 8
- 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: 500
- label_smoothing_factor: 0.1

### Training results



### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6

# bart-large-cnn-qmsum-meeting-summarization

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7578
- Rouge1: 37.9431
- Rouge2: 10.6366
- Rougel: 25.5782
- Rougelsum: 33.0209
- Gen Len: 72.7714 ## 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: 8
- eval_batch_size: 8
- 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: 500
- label_smoothing_factor: 0.1 ### Training results ### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # bart-large-cnn-qmsum-meeting-summarization

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7578
- Rouge1: 37.9431
- Rouge2: 10.6366
- Rougel: 25.5782
- Rougelsum: 33.0209
- Gen Len: 72.7714 ## 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: 8
- eval_batch_size: 8
- 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: 500
- label_smoothing_factor: 0.1 ### Training results ### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #dataset-yawnick/QMSum #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# bart-large-cnn-qmsum-meeting-summarization

This model is a fine-tuned version of facebook/bart-large-cnn on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7578
- Rouge1: 37.9431
- Rouge2: 10.6366
- Rougel: 25.5782
- Rougelsum: 33.0209
- Gen Len: 72.7714## 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: 8
- eval_batch_size: 8
- 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: 500
- label_smoothing_factor: 0.1### Training results### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6