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NASES-clara-med

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metadata
tags:
  - simplification
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: NASES-clara-med
    results: []

NASES-clara-med

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

  • Loss: 3.1300
  • Rouge1: 45.8234
  • Rouge2: 27.9023
  • Rougel: 40.0601
  • Rougelsum: 40.1365

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: 5.6e-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: 30

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum
No log 1.0 190 2.1020 44.2638 26.5513 38.8656 38.9574
No log 2.0 380 2.0341 44.8083 27.3491 39.2192 39.2873
1.8386 3.0 570 2.0883 44.9818 27.5372 39.702 39.7941
1.8386 4.0 760 2.1829 45.3864 28.2511 40.224 40.3422
0.6628 5.0 950 2.3020 45.1704 27.6203 39.6343 39.8564
0.6628 6.0 1140 2.3936 45.245 27.5998 39.5014 39.7109
0.6628 7.0 1330 2.5143 45.419 27.8554 39.7599 39.8859
0.2204 8.0 1520 2.5889 45.3461 27.8309 39.7638 39.7984
0.2204 9.0 1710 2.6406 45.0848 27.6269 39.499 39.5797
0.1278 10.0 1900 2.6651 44.8955 27.1523 39.0341 39.1819
0.1278 11.0 2090 2.7188 44.8196 27.3935 39.1979 39.2786
0.1278 12.0 2280 2.7608 45.3321 27.7995 39.755 39.8835
0.0849 13.0 2470 2.7928 45.3633 27.7144 39.7197 39.8112
0.0849 14.0 2660 2.8243 45.2905 27.8178 39.8823 39.9496
0.0655 15.0 2850 2.8515 45.8272 28.2161 40.2645 40.3717
0.0655 16.0 3040 2.8778 45.4863 27.8831 39.7118 39.7972
0.0655 17.0 3230 2.9078 45.3699 27.8244 39.5926 39.6862
0.0515 18.0 3420 2.9201 45.601 28.0129 40.0033 40.085
0.0515 19.0 3610 2.9525 45.6083 27.8189 39.8085 39.9177
0.0427 20.0 3800 2.9935 45.3061 27.2807 39.3424 39.4389
0.0427 21.0 3990 3.0076 45.2374 27.337 39.5316 39.5977
0.0427 22.0 4180 3.0457 46.1103 27.8833 40.1059 40.1985
0.0272 23.0 4370 3.0433 45.4395 27.8712 40.001 40.0459
0.0272 24.0 4560 3.0483 45.6994 27.6396 39.8601 39.9617
0.0188 25.0 4750 3.0561 45.7877 27.808 40.1369 40.1748
0.0188 26.0 4940 3.0849 45.574 27.6999 39.8785 39.9602
0.0188 27.0 5130 3.1076 45.7393 27.8011 39.9347 39.9889
0.0149 28.0 5320 3.1277 45.6849 27.6962 39.85 39.9121
0.0149 29.0 5510 3.1181 45.8135 27.7591 39.9626 39.9888
0.0119 30.0 5700 3.1300 45.8234 27.9023 40.0601 40.1365

Framework versions

  • Transformers 4.25.1
  • Pytorch 1.13.0
  • Datasets 2.8.0
  • Tokenizers 0.12.1