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# t5-large-cnndaily

This model is a fine-tuned version of [t5-large-cnndaily/checkpoint-10318](https://huggingface.co/t5-large-cnndaily/checkpoint-10318) on the jkorsvik/cnn_daily_mail_nor_final dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.8011
- eval_rouge1: 32.6372
- eval_rouge2: 12.4352
- eval_rougeL: 22.9598
- eval_rougeLsum: 30.3946
- eval_gen_len: 65.1487
- eval_runtime: 167.4595
- eval_samples_per_second: 21.205
- eval_steps_per_second: 0.167
- step: 11792

## Model description

wandb logs: https://wandb.ai/navjordj/huggingface/runs/zrx32j09?workspace=user-navjordj

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: 4
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2

{"tags": ["generated_from_trainer"], "datasets": ["jkorsvik/cnn_daily_mail_nor_final"], "inference": {"parameters": {"max_length": 160}}, "model-index": [{"name": "t5-large-cnndaily-copy", "results": []}]}
transformers pytorch t5 text2text-generation generated_from_trainer dataset:jkorsvik/cnn_daily_mail_nor_final autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-jkorsvik/cnn_daily_mail_nor_final #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# t5-large-cnndaily

This model is a fine-tuned version of t5-large-cnndaily/checkpoint-10318 on the jkorsvik/cnn_daily_mail_nor_final dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.8011
- eval_rouge1: 32.6372
- eval_rouge2: 12.4352
- eval_rougeL: 22.9598
- eval_rougeLsum: 30.3946
- eval_gen_len: 65.1487
- eval_runtime: 167.4595
- eval_samples_per_second: 21.205
- eval_steps_per_second: 0.167
- step: 11792

## Model description

wandb logs: URL

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: 4
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0

### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2

# t5-large-cnndaily

This model is a fine-tuned version of t5-large-cnndaily/checkpoint-10318 on the jkorsvik/cnn_daily_mail_nor_final dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.8011
- eval_rouge1: 32.6372
- eval_rouge2: 12.4352
- eval_rougeL: 22.9598
- eval_rougeLsum: 30.3946
- eval_gen_len: 65.1487
- eval_runtime: 167.4595
- eval_samples_per_second: 21.205
- eval_steps_per_second: 0.167
- step: 11792 ## Model description

wandb logs: URL

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: 4
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-jkorsvik/cnn_daily_mail_nor_final #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5-large-cnndaily

This model is a fine-tuned version of t5-large-cnndaily/checkpoint-10318 on the jkorsvik/cnn_daily_mail_nor_final dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.8011
- eval_rouge1: 32.6372
- eval_rouge2: 12.4352
- eval_rougeL: 22.9598
- eval_rougeLsum: 30.3946
- eval_gen_len: 65.1487
- eval_runtime: 167.4595
- eval_samples_per_second: 21.205
- eval_steps_per_second: 0.167
- step: 11792 ## Model description

wandb logs: URL

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: 4
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0 ### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #t5 #text2text-generation #generated_from_trainer #dataset-jkorsvik/cnn_daily_mail_nor_final #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# t5-large-cnndaily

This model is a fine-tuned version of t5-large-cnndaily/checkpoint-10318 on the jkorsvik/cnn_daily_mail_nor_final dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.8011
- eval_rouge1: 32.6372
- eval_rouge2: 12.4352
- eval_rougeL: 22.9598
- eval_rougeLsum: 30.3946
- eval_gen_len: 65.1487
- eval_runtime: 167.4595
- eval_samples_per_second: 21.205
- eval_steps_per_second: 0.167
- step: 11792## Model description

wandb logs: URL

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: 4
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0### Framework versions

- Transformers 4.27.0.dev0
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2