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

t5-small-mse-summarization

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

  • Loss: 1.1108
  • Rouge1: 43.1145
  • Rouge2: 23.2262
  • Rougel: 37.218
  • Rougelsum: 41.0897
  • Bleurt: -0.8051
  • Gen Len: 18.549

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: 5e-05
  • train_batch_size: 64
  • eval_batch_size: 256
  • seed: 42
  • 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
1.5207 1.0 267 1.2922 38.8738 19.1958 32.8458 36.9993 -0.9061 18.668
1.363 2.0 534 1.2340 39.8466 20.0452 33.9101 37.7708 -0.8925 18.657
1.3062 3.0 801 1.2057 40.5536 20.8249 34.5221 38.4648 -0.8625 18.602
1.272 4.0 1068 1.1782 41.0078 21.2186 35.0101 38.9186 -0.8595 18.602
1.2312 5.0 1335 1.1688 41.521 21.7934 35.704 39.4718 -0.842 18.486
1.2052 6.0 1602 1.1557 42.1037 22.4291 36.3554 40.1124 -0.8432 18.533
1.1842 7.0 1869 1.1440 42.4438 22.6456 36.5729 40.3134 -0.8288 18.553
1.1643 8.0 2136 1.1408 42.245 22.4859 36.3637 40.2193 -0.8284 18.622
1.1495 9.0 2403 1.1320 42.5362 22.5034 36.5092 40.4552 -0.8211 18.57
1.1368 10.0 2670 1.1301 42.5159 22.462 36.4646 40.3968 -0.819 18.538
1.1203 11.0 2937 1.1243 42.2803 22.5963 36.3454 40.2987 -0.8242 18.522
1.1116 12.0 3204 1.1197 42.8078 22.8409 36.7344 40.8186 -0.821 18.565
1.099 13.0 3471 1.1193 42.7423 22.9397 36.7894 40.7298 -0.8125 18.552
1.0976 14.0 3738 1.1176 42.9002 23.2394 37.0215 40.9211 -0.8156 18.568
1.0816 15.0 4005 1.1133 43.0007 23.3093 37.2037 40.9719 -0.8059 18.519
1.084 16.0 4272 1.1146 42.9053 23.2391 37.0542 40.8826 -0.8104 18.533
1.0755 17.0 4539 1.1124 43.0429 23.2773 37.1389 41.0755 -0.8086 18.544
1.0748 18.0 4806 1.1121 43.2243 23.4179 37.2039 41.143 -0.8048 18.548
1.072 19.0 5073 1.1106 43.1776 23.3061 37.3105 41.1392 -0.8039 18.549
1.0671 20.0 5340 1.1108 43.1145 23.2262 37.218 41.0897 -0.8051 18.549

Framework versions

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