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# DistilBART_CNN_GNAD_V3

This model is a fine-tuned version of [Einmalumdiewelt/DistilBART_CNN_GNAD_V3](https://huggingface.co/Einmalumdiewelt/DistilBART_CNN_GNAD_V3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8786
- Rouge1: 27.0297
- Rouge2: 8.1224
- Rougel: 17.9777
- Rougelsum: 22.8827
- Gen Len: 90.3667

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results



### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"language": ["de"], "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "DistilBART_CNN_GNAD_V3", "results": []}]}
transformers pytorch bart text2text-generation generated_from_trainer de autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #region-us 




# DistilBART_CNN_GNAD_V3

This model is a fine-tuned version of Einmalumdiewelt/DistilBART_CNN_GNAD_V3 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8786
- Rouge1: 27.0297
- Rouge2: 8.1224
- Rougel: 17.9777
- Rougelsum: 22.8827
- Gen Len: 90.3667

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results



### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

# DistilBART_CNN_GNAD_V3

This model is a fine-tuned version of Einmalumdiewelt/DistilBART_CNN_GNAD_V3 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8786
- Rouge1: 27.0297
- Rouge2: 8.1224
- Rougel: 17.9777
- Rougelsum: 22.8827
- Gen Len: 90.3667 ## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Training results ### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #region-us 
 # DistilBART_CNN_GNAD_V3

This model is a fine-tuned version of Einmalumdiewelt/DistilBART_CNN_GNAD_V3 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8786
- Rouge1: 27.0297
- Rouge2: 8.1224
- Rougel: 17.9777
- Rougelsum: 22.8827
- Gen Len: 90.3667 ## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Training results ### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #bart #text2text-generation #generated_from_trainer #de #autotrain_compatible #endpoints_compatible #region-us 
# DistilBART_CNN_GNAD_V3

This model is a fine-tuned version of Einmalumdiewelt/DistilBART_CNN_GNAD_V3 on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8786
- Rouge1: 27.0297
- Rouge2: 8.1224
- Rougel: 17.9777
- Rougelsum: 22.8827
- Gen Len: 90.3667## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0### Training results### Framework versions

- Transformers 4.22.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1