readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikihow
  metrics:
  - rouge
  model-index:
  - name: t5-small-finetuned-wikihow_3epoch_b4_lr3e-4
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: wikihow
        type: wikihow
        args: all
      metrics:
      - name: Rouge1
        type: rouge
        value: 27.4024
  ---
  
  <!-- 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-finetuned-wikihow_3epoch_b4_lr3e-4
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wikihow dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.2757
  - Rouge1: 27.4024
  - Rouge2: 10.7065
  - Rougel: 23.3153
  - Rougelsum: 26.7336
  - Gen Len: 18.5506
  
  ## 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: 0.0003
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 2.8424        | 0.13  | 5000   | 2.5695          | 25.2232 | 8.7617  | 21.2019 | 24.4949   | 18.4151 |
  | 2.7334        | 0.25  | 10000  | 2.5229          | 25.3739 | 9.0477  | 21.5054 | 24.7553   | 18.3802 |
  | 2.6823        | 0.38  | 15000  | 2.4857          | 26.341  | 9.6711  | 22.3446 | 25.7256   | 18.449  |
  | 2.6607        | 0.51  | 20000  | 2.4540          | 26.0269 | 9.4722  | 22.0822 | 25.3602   | 18.4704 |
  | 2.6137        | 0.64  | 25000  | 2.4326          | 26.2966 | 9.6815  | 22.4422 | 25.6326   | 18.3517 |
  | 2.6077        | 0.76  | 30000  | 2.4108          | 26.0981 | 9.6221  | 22.1189 | 25.454    | 18.5079 |
  | 2.5847        | 0.89  | 35000  | 2.3879          | 26.2675 | 9.6435  | 22.3738 | 25.6122   | 18.4838 |
  | 2.5558        | 1.02  | 40000  | 2.3827          | 26.3458 | 9.7844  | 22.4718 | 25.7388   | 18.5097 |
  | 2.4902        | 1.14  | 45000  | 2.3725          | 26.4987 | 9.9634  | 22.5398 | 25.8399   | 18.5912 |
  | 2.4785        | 1.27  | 50000  | 2.3549          | 26.884  | 10.1136 | 22.8212 | 26.2262   | 18.4763 |
  | 2.4822        | 1.4   | 55000  | 2.3467          | 26.8635 | 10.2266 | 22.9161 | 26.2252   | 18.5847 |
  | 2.46          | 1.53  | 60000  | 2.3393          | 26.8602 | 10.1785 | 22.8453 | 26.1917   | 18.548  |
  | 2.4523        | 1.65  | 65000  | 2.3330          | 26.91   | 10.237  | 22.9309 | 26.2372   | 18.5154 |
  | 2.4525        | 1.78  | 70000  | 2.3203          | 27.073  | 10.4317 | 23.1355 | 26.4528   | 18.5063 |
  | 2.4566        | 1.91  | 75000  | 2.3109          | 27.3853 | 10.5413 | 23.3455 | 26.7408   | 18.5258 |
  | 2.4234        | 2.03  | 80000  | 2.3103          | 27.0836 | 10.4857 | 23.0538 | 26.409    | 18.5326 |
  | 2.3686        | 2.16  | 85000  | 2.2986          | 27.311  | 10.6038 | 23.3068 | 26.6636   | 18.4874 |
  | 2.3758        | 2.29  | 90000  | 2.2969          | 27.3509 | 10.6502 | 23.2764 | 26.6832   | 18.5438 |
  | 2.3777        | 2.42  | 95000  | 2.2907          | 27.39   | 10.5842 | 23.3601 | 26.7433   | 18.5444 |
  | 2.3624        | 2.54  | 100000 | 2.2875          | 27.3717 | 10.6098 | 23.3326 | 26.7232   | 18.5521 |
  | 2.3543        | 2.67  | 105000 | 2.2811          | 27.4188 | 10.6919 | 23.3022 | 26.7426   | 18.564  |
  | 2.366         | 2.8   | 110000 | 2.2763          | 27.4872 | 10.7079 | 23.4135 | 26.829    | 18.5399 |
  | 2.3565        | 2.93  | 115000 | 2.2757          | 27.4024 | 10.7065 | 23.3153 | 26.7336   | 18.5506 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.0.0
  - Tokenizers 0.11.6
  