
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# pegasus-newsroom-cnn_full-adam8bit

This model is a fine-tuned version of [google/pegasus-newsroom](https://huggingface.co/google/pegasus-newsroom) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9826
- eval_rouge1: 38.2456
- eval_rouge2: 17.3966
- eval_rougeL: 26.9273
- eval_rougeLsum: 35.3265
- eval_gen_len: 69.658
- eval_runtime: 13626.7467
- eval_samples_per_second: 0.183
- eval_steps_per_second: 0.012
- epoch: 0.22
- step: 250

## 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: 0.0016
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

{"tags": ["generated_from_trainer"], "model-index": [{"name": "pegasus-newsroom-cnn_full-adam8bit", "results": []}]}
transformers pytorch pegasus text2text-generation generated_from_trainer autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 




# pegasus-newsroom-cnn_full-adam8bit

This model is a fine-tuned version of google/pegasus-newsroom on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9826
- eval_rouge1: 38.2456
- eval_rouge2: 17.3966
- eval_rougeL: 26.9273
- eval_rougeLsum: 35.3265
- eval_gen_len: 69.658
- eval_runtime: 13626.7467
- eval_samples_per_second: 0.183
- eval_steps_per_second: 0.012
- epoch: 0.22
- step: 250

## 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: 0.0016
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1

# pegasus-newsroom-cnn_full-adam8bit

This model is a fine-tuned version of google/pegasus-newsroom on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9826
- eval_rouge1: 38.2456
- eval_rouge2: 17.3966
- eval_rougeL: 26.9273
- eval_rougeLsum: 35.3265
- eval_gen_len: 69.658
- eval_runtime: 13626.7467
- eval_samples_per_second: 0.183
- eval_steps_per_second: 0.012
- epoch: 0.22
- step: 250 ## 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: 0.0016
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1 ### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
 # pegasus-newsroom-cnn_full-adam8bit

This model is a fine-tuned version of google/pegasus-newsroom on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9826
- eval_rouge1: 38.2456
- eval_rouge2: 17.3966
- eval_rougeL: 26.9273
- eval_rougeLsum: 35.3265
- eval_gen_len: 69.658
- eval_runtime: 13626.7467
- eval_samples_per_second: 0.183
- eval_steps_per_second: 0.012
- epoch: 0.22
- step: 250 ## 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: 0.0016
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1 ### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #pegasus #text2text-generation #generated_from_trainer #autotrain_compatible #endpoints_compatible #region-us 
# pegasus-newsroom-cnn_full-adam8bit

This model is a fine-tuned version of google/pegasus-newsroom on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9826
- eval_rouge1: 38.2456
- eval_rouge2: 17.3966
- eval_rougeL: 26.9273
- eval_rougeLsum: 35.3265
- eval_gen_len: 69.658
- eval_runtime: 13626.7467
- eval_samples_per_second: 0.183
- eval_steps_per_second: 0.012
- epoch: 0.22
- step: 250## 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: 0.0016
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1