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# t5-base-billsum_model

This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the billsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3212
- eval_rouge1: 0.2449
- eval_rouge2: 0.199
- eval_rougeL: 0.2377
- eval_rougeLsum: 0.2377
- eval_gen_len: 18.9997
- eval_bleu: 0.0019
- eval_runtime: 199.0696
- eval_samples_per_second: 16.421
- eval_steps_per_second: 1.03
- epoch: 9.0
- step: 2666

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["billsum"], "model-index": [{"name": "t5-base-billsum_model", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer dataset:billsum license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-billsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 




# t5-base-billsum_model

This model is a fine-tuned version of t5-base on the billsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3212
- eval_rouge1: 0.2449
- eval_rouge2: 0.199
- eval_rougeL: 0.2377
- eval_rougeLsum: 0.2377
- eval_gen_len: 18.9997
- eval_bleu: 0.0019
- eval_runtime: 199.0696
- eval_samples_per_second: 16.421
- eval_steps_per_second: 1.03
- epoch: 9.0
- step: 2666

## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

# t5-base-billsum_model

This model is a fine-tuned version of t5-base on the billsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3212
- eval_rouge1: 0.2449
- eval_rouge2: 0.199
- eval_rougeL: 0.2377
- eval_rougeLsum: 0.2377
- eval_gen_len: 18.9997
- eval_bleu: 0.0019
- eval_runtime: 199.0696
- eval_samples_per_second: 16.421
- eval_steps_per_second: 1.03
- epoch: 9.0
- step: 2666 ## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-billsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5-base-billsum_model

This model is a fine-tuned version of t5-base on the billsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3212
- eval_rouge1: 0.2449
- eval_rouge2: 0.199
- eval_rougeL: 0.2377
- eval_rougeLsum: 0.2377
- eval_gen_len: 18.9997
- eval_bleu: 0.0019
- eval_runtime: 199.0696
- eval_samples_per_second: 16.421
- eval_steps_per_second: 1.03
- epoch: 9.0
- step: 2666 ## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #dataset-billsum #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# t5-base-billsum_model

This model is a fine-tuned version of t5-base on the billsum dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.3212
- eval_rouge1: 0.2449
- eval_rouge2: 0.199
- eval_rougeL: 0.2377
- eval_rougeLsum: 0.2377
- eval_gen_len: 18.9997
- eval_bleu: 0.0019
- eval_runtime: 199.0696
- eval_samples_per_second: 16.421
- eval_steps_per_second: 1.03
- epoch: 9.0
- step: 2666## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3