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# t5-small-finetuned-xsum-finetuned-xsum

This model is a fine-tuned version of [st3rl4nce/t5-small-finetuned-xsum](https://huggingface.co/st3rl4nce/t5-small-finetuned-xsum) on the xsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.5146
- eval_rouge1: 27.7165
- eval_rouge2: 7.3585
- eval_rougeL: 21.7684
- eval_rougeLsum: 21.7758
- eval_gen_len: 18.8131
- eval_runtime: 667.5713
- eval_samples_per_second: 16.975
- eval_steps_per_second: 1.062
- epoch: 0.01
- step: 113

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
{"language": ["en"], "tags": ["generated_from_trainer"], "datasets": ["xsum"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum-finetuned-xsum", "results": [{"task": {"type": "text2text-generation", "name": "Sequence-to-sequence Language Modeling"}, "dataset": {"name": "xsum", "type": "xsum", "args": "default"}, "metrics": [{"type": "rouge", "value": 27.7165, "name": "Rouge1"}]}]}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer en dataset:xsum model-index autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #en #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# t5-small-finetuned-xsum-finetuned-xsum

This model is a fine-tuned version of st3rl4nce/t5-small-finetuned-xsum on the xsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.5146
- eval_rouge1: 27.7165
- eval_rouge2: 7.3585
- eval_rougeL: 21.7684
- eval_rougeLsum: 21.7758
- eval_gen_len: 18.8131
- eval_runtime: 667.5713
- eval_samples_per_second: 16.975
- eval_steps_per_second: 1.062
- epoch: 0.01
- step: 113

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
# t5-small-finetuned-xsum-finetuned-xsum

This model is a fine-tuned version of st3rl4nce/t5-small-finetuned-xsum on the xsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.5146
- eval_rouge1: 27.7165
- eval_rouge2: 7.3585
- eval_rougeL: 21.7684
- eval_rougeLsum: 21.7758
- eval_gen_len: 18.8131
- eval_runtime: 667.5713
- eval_samples_per_second: 16.975
- eval_steps_per_second: 1.062
- epoch: 0.01
- step: 113 ## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #en #dataset-xsum #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5-small-finetuned-xsum-finetuned-xsum

This model is a fine-tuned version of st3rl4nce/t5-small-finetuned-xsum on the xsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.5146
- eval_rouge1: 27.7165
- eval_rouge2: 7.3585
- eval_rougeL: 21.7684
- eval_rougeLsum: 21.7758
- eval_gen_len: 18.8131
- eval_runtime: 667.5713
- eval_samples_per_second: 16.975
- eval_steps_per_second: 1.062
- epoch: 0.01
- step: 113 ## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3