marimari-r2r-mlsum-clara-med

This model is a fine-tuned version of IIC/marimari-r2r-mlsum on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 3.9618
  • Rouge1: 42.6764
  • Rouge2: 24.4569
  • Rougel: 37.0033
  • Rougelsum: 37.1595

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 2.3970 40.7426 23.212 35.7093 35.8437
No log 2.0 380 2.3165 42.5676 24.6494 37.1225 37.2619
1.9699 3.0 570 2.4711 42.0346 23.7633 36.3472 36.4433
1.9699 4.0 760 2.7339 41.1717 22.8419 35.3263 35.4823
0.6485 5.0 950 2.9593 40.714 22.6931 34.8859 35.0647
0.6485 6.0 1140 3.1316 41.3218 23.2054 35.3103 35.5063
0.6485 7.0 1330 3.2542 41.2786 23.4853 35.8236 35.972
0.1529 8.0 1520 3.3470 41.2991 22.8385 35.0524 35.2153
0.1529 9.0 1710 3.4324 41.3838 23.1045 35.3472 35.5779
0.0719 10.0 1900 3.5187 42.0833 23.8538 36.3282 36.5294
0.0719 11.0 2090 3.5527 41.2993 23.0323 35.3116 35.4687
0.0719 12.0 2280 3.6624 41.6524 23.8925 35.9281 36.1012
0.0393 13.0 2470 3.6536 41.188 23.2066 35.371 35.5616
0.0393 14.0 2660 3.6656 40.8222 22.5651 35.0515 35.1399
0.0266 15.0 2850 3.7349 41.844 23.7839 36.102 36.3169
0.0266 16.0 3040 3.7254 41.5535 23.3996 35.9619 36.0981
0.0266 17.0 3230 3.7919 41.5683 23.2824 36.0855 36.2475
0.0151 18.0 3420 3.8152 42.1272 24.0548 36.5784 36.785
0.0151 19.0 3610 3.8213 41.9185 23.5975 36.1182 36.3194
0.0087 20.0 3800 3.8501 41.3409 23.0081 35.7662 35.9451
0.0087 21.0 3990 3.8690 41.9496 23.7032 36.0116 36.1843
0.0087 22.0 4180 3.8809 42.5366 24.6413 37.2644 37.459
0.0044 23.0 4370 3.8865 42.4346 24.2278 36.7284 36.8846
0.0044 24.0 4560 3.9044 42.9781 24.8423 37.3582 37.4807
0.0024 25.0 4750 3.9138 42.6738 24.4737 36.8959 37.0031
0.0024 26.0 4940 3.9361 42.5267 24.4155 36.8414 36.9915
0.0024 27.0 5130 3.9477 42.4844 24.5483 36.8857 37.0219
0.0013 28.0 5320 3.9561 42.7199 24.5977 37.1206 37.2374
0.0013 29.0 5510 3.9599 42.7088 24.4474 37.0513 37.1971
0.001 30.0 5700 3.9618 42.6764 24.4569 37.0033 37.1595

Framework versions

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