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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-3
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: wikihow
          type: wikihow
          args: all
        metrics:
          - name: Rouge1
            type: rouge
            value: 27.1711

t5-small-finetuned-wikihow_3epoch_b8_lr3e-3

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

  • Loss: 2.3163
  • Rouge1: 27.1711
  • Rouge2: 10.6296
  • Rougel: 23.206
  • Rougelsum: 26.4801
  • Gen Len: 18.5433

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: 0.003
  • 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
3.0734 0.25 5000 2.7884 22.4825 7.2492 19.243 21.9167 18.0616
2.9201 0.51 10000 2.7089 24.0869 8.0348 20.4814 23.4541 18.5994
2.8403 0.76 15000 2.6390 24.62 8.3776 20.8736 23.9784 18.4676
2.7764 1.02 20000 2.5943 24.1504 8.3933 20.8271 23.5382 18.4078
2.6641 1.27 25000 2.5428 25.6574 9.2371 21.8576 24.9558 18.4249
2.6369 1.53 30000 2.5042 25.5208 9.254 21.6673 24.8589 18.6467
2.6 1.78 35000 2.4637 26.094 9.7003 22.3097 25.4695 18.5065
2.5562 2.03 40000 2.4285 26.5374 9.9222 22.5291 25.8836 18.5553
2.4322 2.29 45000 2.3858 26.939 10.3555 23.0211 26.2834 18.5614
2.4106 2.54 50000 2.3537 26.7423 10.2816 22.7986 26.083 18.5792
2.3731 2.8 55000 2.3163 27.1711 10.6296 23.206 26.4801 18.5433

Framework versions

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