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T5_base_title_v2

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metadata
license: apache-2.0
base_model: t5-base
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: T5_base_title_v2
    results: []

T5_base_title_v2

This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0995
  • Rouge1: 0.3574
  • Rouge2: 0.1666
  • Rougel: 0.3037
  • Rougelsum: 0.303
  • Gen Len: 16.495

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
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 100 2.1695 0.3249 0.1495 0.2795 0.2798 17.315
No log 2.0 200 2.0994 0.3595 0.1696 0.3078 0.3085 16.825
No log 3.0 300 2.0724 0.3679 0.1836 0.312 0.3131 16.525
No log 4.0 400 2.0745 0.3669 0.1767 0.3137 0.3141 16.505
2.0908 5.0 500 2.0567 0.3725 0.181 0.3205 0.3211 16.545
2.0908 6.0 600 2.0575 0.3654 0.174 0.3101 0.3097 16.62
2.0908 7.0 700 2.0640 0.3475 0.1649 0.2959 0.2956 16.485
2.0908 8.0 800 2.0588 0.3678 0.1827 0.312 0.3113 16.54
2.0908 9.0 900 2.0615 0.3654 0.1774 0.3106 0.3098 16.565
1.696 10.0 1000 2.0689 0.3654 0.1767 0.3077 0.3069 16.78
1.696 11.0 1100 2.0767 0.3633 0.1736 0.309 0.3078 16.57
1.696 12.0 1200 2.0749 0.366 0.1802 0.3147 0.3145 16.755
1.696 13.0 1300 2.0782 0.3632 0.1714 0.3117 0.3111 16.95
1.696 14.0 1400 2.0841 0.3637 0.1718 0.3118 0.3111 16.855
1.5311 15.0 1500 2.0873 0.3618 0.1713 0.3073 0.307 16.57
1.5311 16.0 1600 2.0940 0.3655 0.1714 0.3115 0.3111 16.625
1.5311 17.0 1700 2.0943 0.3619 0.1683 0.3089 0.3082 16.525
1.5311 18.0 1800 2.0981 0.3609 0.1697 0.3074 0.3065 16.44
1.5311 19.0 1900 2.0990 0.3567 0.1665 0.3047 0.3036 16.47
1.447 20.0 2000 2.0995 0.3574 0.1666 0.3037 0.303 16.495

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1