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---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-translated-lithuanian-simplifier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-translated-lithuanian-simplifier
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/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
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