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mt5-translated-lithuanian-simplifier

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metadata
license: apache-2.0
base_model: google/mt5-base
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: mt5-translated-lithuanian-simplifier
    results: []

mt5-translated-lithuanian-simplifier

This model is a fine-tuned version of google/mt5-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0761
  • Rouge1: 0.7877
  • Rouge2: 0.6566
  • Rougel: 0.7845
  • Gen Len: 49.2293

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: 0.0001
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 8

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Gen Len
23.9322 0.1 200 19.1649 0.016 0.0004 0.0146 512.0
2.5416 0.19 400 1.4406 0.035 0.0002 0.0345 51.3394
0.7449 0.29 600 0.7221 0.0021 0.0 0.0021 50.2293
0.4405 0.38 800 0.2164 0.5491 0.3593 0.5367 49.4955
0.177 0.48 1000 0.1672 0.6294 0.4636 0.6209 49.2293
0.1838 0.57 1200 0.1561 0.6214 0.4375 0.613 49.2293
0.1471 0.67 1400 0.1295 0.7071 0.5673 0.6998 49.2293
0.1622 0.77 1600 0.1229 0.6929 0.5402 0.6858 49.2293
0.1255 0.86 1800 0.1192 0.7044 0.5547 0.6978 49.2293
0.1281 0.96 2000 0.1150 0.7169 0.5718 0.7103 49.2293
0.1561 1.05 2200 0.1088 0.7165 0.5688 0.7108 49.2293
0.145 1.15 2400 0.1064 0.7321 0.5921 0.7263 49.2293
0.1207 1.25 2600 0.1030 0.7348 0.5957 0.7291 49.2293
0.1151 1.34 2800 0.1014 0.7289 0.5859 0.7239 49.2293
0.1001 1.44 3000 0.0983 0.7402 0.6003 0.7349 49.2293
0.1354 1.53 3200 0.0963 0.738 0.598 0.7332 49.2293
0.1092 1.63 3400 0.0978 0.7446 0.607 0.7394 49.2293
0.1109 1.72 3600 0.0973 0.7427 0.6034 0.7377 49.2293
0.1083 1.82 3800 0.0950 0.7479 0.6094 0.7432 49.2293
0.1348 1.92 4000 0.0958 0.7498 0.6121 0.745 49.2293
0.1004 2.01 4200 0.0898 0.7539 0.6152 0.7494 49.2293
0.1131 2.11 4400 0.0925 0.753 0.6154 0.7488 49.2293
0.1312 2.2 4600 0.0919 0.755 0.6183 0.7508 49.2293
0.1139 2.3 4800 0.0908 0.756 0.6182 0.7518 49.2293
0.1168 2.39 5000 0.0880 0.7574 0.6202 0.7533 49.2293
0.0793 2.49 5200 0.0897 0.7575 0.6193 0.7531 49.2293
0.0869 2.59 5400 0.0866 0.7605 0.6228 0.7564 49.2293
0.1053 2.68 5600 0.0870 0.7594 0.6203 0.7551 49.2293
0.0889 2.78 5800 0.0893 0.7609 0.6237 0.7568 49.2293
0.0982 2.87 6000 0.0873 0.7637 0.6279 0.7599 49.2293
0.0838 2.97 6200 0.0846 0.7665 0.6309 0.7626 49.2293
0.0829 3.07 6400 0.0844 0.7665 0.6315 0.7629 49.2293
0.068 3.16 6600 0.0836 0.7695 0.6358 0.7658 49.2293
0.0747 3.26 6800 0.0848 0.7675 0.6322 0.7639 49.2293
0.0792 3.35 7000 0.0840 0.7691 0.6342 0.7656 49.2293
0.0739 3.45 7200 0.0820 0.7713 0.6365 0.7676 49.2293
0.0793 3.54 7400 0.0813 0.7723 0.6374 0.7685 49.2293
0.0908 3.64 7600 0.0819 0.7731 0.6388 0.7696 49.2293
0.1125 3.74 7800 0.0811 0.774 0.6402 0.7705 49.2293
0.1231 3.83 8000 0.0805 0.7736 0.6391 0.7699 49.2293
0.0805 3.93 8200 0.0806 0.7736 0.6383 0.7698 49.2293
0.0798 4.02 8400 0.0806 0.7758 0.6413 0.7726 49.2293
0.061 4.12 8600 0.0807 0.7738 0.6391 0.7705 49.2293
0.0636 4.21 8800 0.0810 0.7763 0.6424 0.7731 49.2293
0.0813 4.31 9000 0.0798 0.7765 0.6418 0.7731 49.2293
0.0664 4.41 9200 0.0804 0.7779 0.6441 0.7744 49.2293
0.077 4.5 9400 0.0783 0.7775 0.6432 0.774 49.2293
0.0769 4.6 9600 0.0788 0.7786 0.6446 0.7752 49.2293
0.0874 4.69 9800 0.0796 0.7782 0.6455 0.7749 49.2293
0.0682 4.79 10000 0.0784 0.7783 0.6452 0.7752 49.2293
0.0649 4.89 10200 0.0781 0.7788 0.6453 0.7757 49.2293
0.0594 4.98 10400 0.0791 0.7795 0.6468 0.7762 49.2293
0.1001 5.08 10600 0.0775 0.7794 0.6464 0.7762 49.2293
0.065 5.17 10800 0.0794 0.7794 0.6474 0.7762 49.2293
0.0505 5.27 11000 0.0787 0.7809 0.6481 0.7775 49.2293
0.0904 5.36 11200 0.0772 0.7825 0.6504 0.7793 49.2293
0.0782 5.46 11400 0.0777 0.7835 0.651 0.7803 49.2293
0.0758 5.56 11600 0.0774 0.7823 0.6505 0.7792 49.2293
0.0685 5.65 11800 0.0778 0.7819 0.6498 0.7787 49.2293
0.0664 5.75 12000 0.0774 0.7818 0.6493 0.7786 49.2293
0.0841 5.84 12200 0.0770 0.7848 0.6527 0.7813 49.2293
0.0867 5.94 12400 0.0765 0.7844 0.6522 0.7812 49.2293
0.0572 6.03 12600 0.0772 0.7849 0.6522 0.7816 49.2293
0.0554 6.13 12800 0.0775 0.7844 0.6526 0.7812 49.2293
0.0725 6.23 13000 0.0774 0.7851 0.6534 0.7822 49.2293
0.0952 6.32 13200 0.0778 0.7848 0.6527 0.7817 49.2293
0.0795 6.42 13400 0.0764 0.7858 0.6542 0.7826 49.2293
0.0682 6.51 13600 0.0772 0.7852 0.6527 0.7819 49.2293
0.0483 6.61 13800 0.0777 0.785 0.6525 0.7815 49.2293
0.0725 6.7 14000 0.0767 0.7864 0.6545 0.7831 49.2293
0.0675 6.8 14200 0.0773 0.786 0.6551 0.7827 49.2293
0.0706 6.9 14400 0.0758 0.7867 0.6556 0.7837 49.2293
0.0785 6.99 14600 0.0772 0.7866 0.6559 0.7835 49.2293
0.0796 7.09 14800 0.0763 0.7872 0.6564 0.7841 49.2293
0.0761 7.18 15000 0.0757 0.7879 0.6566 0.7848 49.2293
0.0598 7.28 15200 0.0758 0.788 0.6568 0.7849 49.2293
0.0587 7.38 15400 0.0768 0.7872 0.6556 0.7839 49.2293
0.0859 7.47 15600 0.0765 0.7875 0.6559 0.7842 49.2293
0.061 7.57 15800 0.0764 0.7876 0.6564 0.7845 49.2293
0.0718 7.66 16000 0.0764 0.7871 0.6558 0.784 49.2293
0.0695 7.76 16200 0.0763 0.7873 0.656 0.7842 49.2293
0.0678 7.85 16400 0.0762 0.7875 0.6565 0.7844 49.2293
0.0751 7.95 16600 0.0761 0.7877 0.6566 0.7845 49.2293

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.1
  • Datasets 2.16.1
  • Tokenizers 0.15.0