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# tst-summarization

This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 56.239
- Rouge2: 28.9873
- Rougel: 38.5242
- Rougelsum: 53.7902
- Gen Len: 105.2973

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.4
- Tokenizers 0.11.6

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "tst-summarization", "results": []}]}
transformers pytorch bart text2text-generation generated_from_trainer license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 




# tst-summarization

This model is a fine-tuned version of philschmid/bart-large-cnn-samsum on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 56.239
- Rouge2: 28.9873
- Rougel: 38.5242
- Rougelsum: 53.7902
- Gen Len: 105.2973

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results



### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.4
- Tokenizers 0.11.6

# tst-summarization

This model is a fine-tuned version of philschmid/bart-large-cnn-samsum on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 56.239
- Rouge2: 28.9873
- Rougel: 38.5242
- Rougelsum: 53.7902
- Gen Len: 105.2973 ## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.4
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # tst-summarization

This model is a fine-tuned version of philschmid/bart-large-cnn-samsum on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 56.239
- Rouge2: 28.9873
- Rougel: 38.5242
- Rougelsum: 53.7902
- Gen Len: 105.2973 ## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0 ### Training results ### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.4
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# tst-summarization

This model is a fine-tuned version of philschmid/bart-large-cnn-samsum on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 56.239
- Rouge2: 28.9873
- Rougel: 38.5242
- Rougelsum: 53.7902
- Gen Len: 105.2973## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0### Training results### Framework versions

- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.4
- Tokenizers 0.11.6