readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: t5-small-t5small-gigaword
    results: []
  ---
  
  <!-- 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. -->
  
  # t5-small-t5small-gigaword
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4052
  - Rouge1: 50.1555
  - Rouge2: 25.5096
  - Rougel: 46.5771
  - Rougelsum: 46.5827
  - Gen Len: 14.246
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 1.9066        | 1.0   | 118874 | 1.4971          | 49.2994 | 24.75   | 45.8251 | 45.8162   | 14.3197 |
  | 1.8339        | 2.0   | 237748 | 1.4449          | 49.6767 | 25.1673 | 46.1631 | 46.156    | 14.2557 |
  | 1.8067        | 3.0   | 356622 | 1.4220          | 50.043  | 25.4886 | 46.4577 | 46.437    | 14.2857 |
  | 1.8141        | 4.0   | 475496 | 1.4097          | 50.11   | 25.4327 | 46.502  | 46.5001   | 14.2653 |
  | 1.7985        | 5.0   | 594370 | 1.4052          | 50.1555 | 25.5096 | 46.5771 | 46.5827   | 14.246  |
  
  
  ### Framework versions
  
  - Transformers 4.11.0.dev0
  - Pytorch 1.8.1+cu101
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  