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

t5-base-mse-summarization

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

  • Loss: 0.8743
  • Rouge1: 45.9597
  • Rouge2: 26.8086
  • Rougel: 39.935
  • Rougelsum: 43.8897
  • Bleurt: -0.7132
  • Gen Len: 18.464

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: 1e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Bleurt Gen Len
1.2568 1.0 267 1.0472 41.6829 21.9654 35.4264 39.5556 -0.8231 18.522
1.1085 2.0 534 0.9840 43.1479 23.3351 36.9244 40.886 -0.7843 18.534
1.0548 3.0 801 0.9515 44.1511 24.4912 37.9549 41.9984 -0.7702 18.528
1.0251 4.0 1068 0.9331 44.426 24.9439 38.2978 42.1731 -0.7633 18.619
0.9888 5.0 1335 0.9201 45.0385 25.524 38.8681 42.8998 -0.7497 18.523
0.9623 6.0 1602 0.9119 44.8648 25.469 38.9281 42.7798 -0.7496 18.537
0.9502 7.0 1869 0.9015 44.9668 25.5041 38.9463 42.9368 -0.7412 18.48
0.9316 8.0 2136 0.8973 45.3028 25.7232 39.1533 43.277 -0.7318 18.523
0.9191 9.0 2403 0.8921 45.2901 25.916 39.2909 43.3022 -0.7296 18.529
0.9122 10.0 2670 0.8889 45.3535 26.1369 39.4861 43.28 -0.7271 18.545
0.8993 11.0 2937 0.8857 45.5345 26.1669 39.5656 43.4664 -0.7269 18.474
0.8905 12.0 3204 0.8816 45.7796 26.4145 39.8117 43.734 -0.7185 18.503
0.8821 13.0 3471 0.8794 45.7163 26.4314 39.719 43.6407 -0.7211 18.496
0.8789 14.0 3738 0.8784 45.9097 26.7281 39.9071 43.8105 -0.7127 18.452
0.8665 15.0 4005 0.8765 46.1148 26.8882 40.1006 43.988 -0.711 18.443
0.8676 16.0 4272 0.8766 45.9119 26.7674 39.9001 43.8237 -0.718 18.491
0.8637 17.0 4539 0.8758 45.9158 26.7153 39.9463 43.8323 -0.7183 18.492
0.8622 18.0 4806 0.8752 45.9508 26.75 39.9533 43.8795 -0.7144 18.465
0.8588 19.0 5073 0.8744 45.9192 26.7352 39.8921 43.8204 -0.7148 18.462
0.8554 20.0 5340 0.8743 45.9597 26.8086 39.935 43.8897 -0.7132 18.464

Framework versions

  • Transformers 4.21.2
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1