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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - wikihow
metrics:
  - rouge
model-index:
  - name: t5-small-finetuned-wikihow_3epoch_b8_lr3e-5
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: wikihow
          type: wikihow
          args: all
        metrics:
          - name: Rouge1
            type: rouge
            value: 25.9411

t5-small-finetuned-wikihow_3epoch_b8_lr3e-5

This model is a fine-tuned version of t5-small on the wikihow dataset. It achieves the following results on the evaluation set:

  • Loss: 2.4836
  • Rouge1: 25.9411
  • Rouge2: 9.226
  • Rougel: 21.9087
  • Rougelsum: 25.2863
  • Gen Len: 18.4076

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: 3e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • 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
2.912 0.25 5000 2.6285 23.6659 7.8535 19.9837 22.9884 18.3867
2.8115 0.51 10000 2.5820 24.7979 8.4888 20.8719 24.1321 18.3292
2.767 0.76 15000 2.5555 25.0857 8.6437 21.149 24.4256 18.2981
2.742 1.02 20000 2.5330 25.3431 8.8393 21.425 24.7032 18.3749
2.7092 1.27 25000 2.5203 25.5338 8.9281 21.5378 24.9045 18.3399
2.6989 1.53 30000 2.5065 25.4792 8.9745 21.4941 24.8458 18.4565
2.6894 1.78 35000 2.5018 25.6815 9.1218 21.6958 25.0557 18.406
2.6897 2.03 40000 2.4944 25.8241 9.2127 21.8205 25.1801 18.4228
2.6664 2.29 45000 2.4891 25.8241 9.1662 21.7807 25.1615 18.4258
2.6677 2.54 50000 2.4855 25.7435 9.145 21.765 25.0858 18.4329
2.6631 2.8 55000 2.4836 25.9411 9.226 21.9087 25.2863 18.4076

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.10.0+cu111
  • Datasets 2.0.0
  • Tokenizers 0.11.6