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bart-pubhealth-expanded

This model is a fine-tuned version of facebook/bart-large on the clupubhealth dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3926
  • Rouge1: 29.8528
  • Rouge2: 10.8495
  • Rougel: 23.3682
  • Rougelsum: 23.7565
  • Gen Len: 19.85

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: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
2.7469 0.26 500 2.0845 30.9611 10.7145 23.9719 24.1042 19.905
2.5524 0.51 1000 2.0628 32.0352 11.8898 24.9032 25.1368 19.895
2.429 0.77 1500 2.0787 32.2632 12.0353 25.1245 25.3728 19.895
2.2234 1.03 2000 2.1178 30.6437 11.5713 24.9071 25.1126 19.955
2.1249 1.29 2500 2.1183 31.6095 11.6573 25.0593 25.2063 19.87
2.0302 1.54 3000 2.1319 30.7417 11.4924 24.6388 24.8722 19.895
1.9761 1.8 3500 2.1850 31.6709 11.3036 24.4853 24.7571 19.87
1.8279 2.06 4000 2.2092 31.5778 11.59 24.7599 24.9956 19.825
1.8083 2.32 4500 2.1781 31.0441 10.7513 24.0656 24.3112 19.89
1.7527 2.57 5000 2.2155 31.1191 11.4519 24.4673 24.7157 19.81
1.723 2.83 5500 2.2024 31.9787 12.3158 24.9863 25.2597 19.94
1.5975 3.09 6000 2.2567 31.236 10.9733 24.1302 24.3433 19.9
1.5933 3.35 6500 2.2425 31.022 11.0249 24.1257 24.3555 19.92
1.5792 3.6 7000 2.2428 29.8844 10.3622 23.0802 23.4003 19.96
1.5718 3.86 7500 2.2367 31.2369 11.3854 24.8528 25.1287 19.815
1.4467 4.12 8000 2.2988 30.4903 10.4057 23.9914 24.239 19.715
1.4458 4.37 8500 2.2738 31.4345 11.2989 24.4239 24.6047 19.75
1.4342 4.63 9000 2.3092 28.8421 10.5744 23.0084 23.1741 19.855
1.4416 4.89 9500 2.2747 31.7111 11.5903 24.3422 24.6867 19.945
1.3437 5.15 10000 2.3203 31.11 11.0 24.6098 24.7362 19.81
1.3525 5.4 10500 2.3085 29.414 10.3412 23.3134 23.6552 19.935
1.3533 5.66 11000 2.3123 31.321 11.2686 23.9922 24.336 19.77
1.3248 5.92 11500 2.2916 30.8841 10.779 23.9407 24.0865 19.845
1.2617 6.18 12000 2.3530 29.7167 10.3162 23.4805 23.724 19.93
1.2846 6.43 12500 2.3712 28.3334 9.8425 22.1151 22.2951 19.92
1.2472 6.69 13000 2.3378 29.563 10.0033 23.1863 23.5065 19.865
1.2934 6.95 13500 2.3262 29.137 10.1232 22.9234 23.3799 19.855
1.2136 7.21 14000 2.3640 29.753 10.4865 23.4892 23.8778 19.885
1.2096 7.46 14500 2.3654 29.512 10.3891 23.0427 23.3684 19.88
1.211 7.72 15000 2.3491 30.9014 10.9117 24.127 24.3518 19.785
1.1954 7.98 15500 2.3626 29.0622 10.5405 22.7407 22.9454 19.84
1.1756 8.23 16000 2.3759 29.5277 10.2961 22.7888 23.1239 19.88
1.1516 8.49 16500 2.3772 29.3161 10.1894 23.0404 23.486 19.885
1.1604 8.75 17000 2.3710 29.6161 10.3543 22.8748 23.1849 19.905
1.1639 9.01 17500 2.3889 30.2817 10.8654 23.6438 23.8639 19.895
1.12 9.26 18000 2.3968 28.8747 9.8686 22.2775 22.6541 19.895
1.1136 9.52 18500 2.3950 30.1197 10.8992 23.2575 23.5732 19.86
1.1437 9.78 19000 2.3926 29.8528 10.8495 23.3682 23.7565 19.85

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.7.1
  • Tokenizers 0.13.2
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