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---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-lithuanian-simplifier-full
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-lithuanian-simplifier-full
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: 0.0771
- Rouge1: 0.7828
- Rouge2: 0.6494
- Rougel: 0.7787
- Gen Len: 48.0191
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-------:|
| 24.351 | 0.08 | 200 | 18.6244 | 0.0226 | 0.0018 | 0.0207 | 512.0 |
| 3.0331 | 0.16 | 400 | 0.6830 | 0.0549 | 0.0018 | 0.0497 | 49.0191 |
| 0.2076 | 0.24 | 600 | 0.1642 | 0.6417 | 0.4986 | 0.6328 | 48.0191 |
| 0.2019 | 0.32 | 800 | 0.1303 | 0.6713 | 0.5243 | 0.6633 | 48.0191 |
| 0.1573 | 0.4 | 1000 | 0.1242 | 0.7007 | 0.5589 | 0.6937 | 48.0191 |
| 0.1687 | 0.48 | 1200 | 0.1158 | 0.712 | 0.569 | 0.7055 | 48.0191 |
| 0.1315 | 0.56 | 1400 | 0.1225 | 0.6923 | 0.5361 | 0.6851 | 48.0191 |
| 0.1376 | 0.64 | 1600 | 0.1108 | 0.7171 | 0.5695 | 0.7105 | 48.0191 |
| 0.158 | 0.72 | 1800 | 0.1074 | 0.7229 | 0.574 | 0.7169 | 48.0191 |
| 0.1221 | 0.8 | 2000 | 0.1064 | 0.7227 | 0.5761 | 0.7166 | 48.0191 |
| 0.1371 | 0.88 | 2200 | 0.1049 | 0.7282 | 0.5827 | 0.7223 | 48.0191 |
| 0.1376 | 0.96 | 2400 | 0.1043 | 0.73 | 0.5861 | 0.7239 | 48.0191 |
| 0.1116 | 1.04 | 2600 | 0.1021 | 0.733 | 0.5888 | 0.727 | 48.0191 |
| 0.132 | 1.12 | 2800 | 0.1012 | 0.7338 | 0.5899 | 0.7277 | 48.0191 |
| 0.131 | 1.2 | 3000 | 0.0997 | 0.7365 | 0.5936 | 0.7307 | 48.0191 |
| 0.1001 | 1.28 | 3200 | 0.0950 | 0.7408 | 0.5977 | 0.7355 | 48.0191 |
| 0.1398 | 1.36 | 3400 | 0.0964 | 0.7418 | 0.599 | 0.7364 | 48.0191 |
| 0.1085 | 1.44 | 3600 | 0.0962 | 0.744 | 0.6015 | 0.7386 | 48.0191 |
| 0.097 | 1.52 | 3800 | 0.0967 | 0.743 | 0.6009 | 0.7377 | 48.0191 |
| 0.1178 | 1.6 | 4000 | 0.0955 | 0.7446 | 0.6035 | 0.7391 | 48.0191 |
| 0.1214 | 1.68 | 4200 | 0.0939 | 0.7452 | 0.6036 | 0.7403 | 48.0191 |
| 0.1539 | 1.76 | 4400 | 0.0909 | 0.7486 | 0.6068 | 0.7436 | 48.0191 |
| 0.1141 | 1.83 | 4600 | 0.0900 | 0.7518 | 0.6104 | 0.7467 | 48.0191 |
| 0.0795 | 1.91 | 4800 | 0.0891 | 0.7513 | 0.6097 | 0.7466 | 48.0191 |
| 0.0856 | 1.99 | 5000 | 0.0915 | 0.7513 | 0.6099 | 0.7463 | 48.0191 |
| 0.0954 | 2.07 | 5200 | 0.0898 | 0.753 | 0.6126 | 0.7482 | 48.0191 |
| 0.1271 | 2.15 | 5400 | 0.0901 | 0.7534 | 0.6125 | 0.7486 | 48.0191 |
| 0.0816 | 2.23 | 5600 | 0.0893 | 0.7553 | 0.6148 | 0.7506 | 48.0191 |
| 0.0922 | 2.31 | 5800 | 0.0881 | 0.7569 | 0.6163 | 0.7521 | 48.0191 |
| 0.1177 | 2.39 | 6000 | 0.0878 | 0.7575 | 0.6176 | 0.7532 | 48.0191 |
| 0.0916 | 2.47 | 6200 | 0.0874 | 0.7585 | 0.618 | 0.7541 | 48.0191 |
| 0.1349 | 2.55 | 6400 | 0.0861 | 0.76 | 0.62 | 0.7555 | 48.0191 |
| 0.1196 | 2.63 | 6600 | 0.0833 | 0.7617 | 0.6212 | 0.7572 | 48.0191 |
| 0.0841 | 2.71 | 6800 | 0.0848 | 0.7621 | 0.6219 | 0.7576 | 48.0191 |
| 0.0934 | 2.79 | 7000 | 0.0854 | 0.7622 | 0.6227 | 0.7577 | 48.0191 |
| 0.1246 | 2.87 | 7200 | 0.0835 | 0.7652 | 0.6256 | 0.7606 | 48.0191 |
| 0.0762 | 2.95 | 7400 | 0.0835 | 0.7649 | 0.6262 | 0.7606 | 48.0191 |
| 0.0924 | 3.03 | 7600 | 0.0828 | 0.7662 | 0.6276 | 0.7618 | 48.0191 |
| 0.0822 | 3.11 | 7800 | 0.0834 | 0.7664 | 0.6284 | 0.7621 | 48.0191 |
| 0.0856 | 3.19 | 8000 | 0.0836 | 0.7647 | 0.627 | 0.7603 | 48.0191 |
| 0.0798 | 3.27 | 8200 | 0.0829 | 0.7657 | 0.6284 | 0.7614 | 48.0191 |
| 0.0959 | 3.35 | 8400 | 0.0828 | 0.7671 | 0.6302 | 0.7629 | 48.0191 |
| 0.0871 | 3.43 | 8600 | 0.0820 | 0.7672 | 0.6297 | 0.763 | 48.0191 |
| 0.1068 | 3.51 | 8800 | 0.0827 | 0.7683 | 0.6307 | 0.7641 | 48.0191 |
| 0.072 | 3.59 | 9000 | 0.0820 | 0.7684 | 0.632 | 0.764 | 48.0191 |
| 0.0964 | 3.67 | 9200 | 0.0838 | 0.7692 | 0.6333 | 0.7645 | 48.0191 |
| 0.0946 | 3.75 | 9400 | 0.0809 | 0.7707 | 0.6348 | 0.7663 | 48.0191 |
| 0.0822 | 3.83 | 9600 | 0.0825 | 0.7708 | 0.6347 | 0.7666 | 48.0191 |
| 0.1019 | 3.91 | 9800 | 0.0788 | 0.7733 | 0.6373 | 0.7692 | 48.0191 |
| 0.08 | 3.99 | 10000 | 0.0797 | 0.7727 | 0.6369 | 0.7686 | 48.0191 |
| 0.0989 | 4.07 | 10200 | 0.0818 | 0.7724 | 0.6367 | 0.7681 | 48.0191 |
| 0.0693 | 4.15 | 10400 | 0.0804 | 0.7737 | 0.6378 | 0.7697 | 48.0191 |
| 0.0763 | 4.23 | 10600 | 0.0814 | 0.7741 | 0.6379 | 0.7699 | 48.0191 |
| 0.0956 | 4.31 | 10800 | 0.0815 | 0.7726 | 0.6369 | 0.7683 | 48.0191 |
| 0.0728 | 4.39 | 11000 | 0.0800 | 0.7738 | 0.6374 | 0.7696 | 48.0191 |
| 0.0652 | 4.47 | 11200 | 0.0795 | 0.7747 | 0.6388 | 0.7708 | 48.0191 |
| 0.0706 | 4.55 | 11400 | 0.0798 | 0.7742 | 0.6388 | 0.7703 | 48.0191 |
| 0.0979 | 4.63 | 11600 | 0.0788 | 0.7748 | 0.6387 | 0.7708 | 48.0191 |
| 0.0771 | 4.71 | 11800 | 0.0797 | 0.775 | 0.6402 | 0.771 | 48.0191 |
| 0.1067 | 4.79 | 12000 | 0.0779 | 0.7757 | 0.6404 | 0.7717 | 48.0191 |
| 0.0773 | 4.87 | 12200 | 0.0783 | 0.7759 | 0.6411 | 0.7721 | 48.0191 |
| 0.0866 | 4.95 | 12400 | 0.0780 | 0.7773 | 0.6437 | 0.7734 | 48.0191 |
| 0.0611 | 5.03 | 12600 | 0.0785 | 0.7761 | 0.6418 | 0.7723 | 48.0191 |
| 0.0685 | 5.11 | 12800 | 0.0781 | 0.777 | 0.6421 | 0.773 | 48.0191 |
| 0.0501 | 5.19 | 13000 | 0.0788 | 0.7764 | 0.6411 | 0.7721 | 48.0191 |
| 0.0626 | 5.27 | 13200 | 0.0792 | 0.7762 | 0.6416 | 0.7721 | 48.0191 |
| 0.0708 | 5.35 | 13400 | 0.0795 | 0.7761 | 0.6408 | 0.772 | 48.0191 |
| 0.055 | 5.42 | 13600 | 0.0779 | 0.7773 | 0.642 | 0.7733 | 48.0191 |
| 0.0749 | 5.5 | 13800 | 0.0789 | 0.7783 | 0.6431 | 0.7742 | 48.0191 |
| 0.0771 | 5.58 | 14000 | 0.0779 | 0.778 | 0.6437 | 0.774 | 48.0191 |
| 0.0906 | 5.66 | 14200 | 0.0779 | 0.7781 | 0.6431 | 0.7742 | 48.0191 |
| 0.0679 | 5.74 | 14400 | 0.0778 | 0.7783 | 0.6449 | 0.7745 | 48.0191 |
| 0.0605 | 5.82 | 14600 | 0.0786 | 0.7778 | 0.6439 | 0.7738 | 48.0191 |
| 0.0647 | 5.9 | 14800 | 0.0781 | 0.7785 | 0.6445 | 0.7743 | 48.0191 |
| 0.058 | 5.98 | 15000 | 0.0775 | 0.7792 | 0.6448 | 0.7749 | 48.0191 |
| 0.0574 | 6.06 | 15200 | 0.0788 | 0.7793 | 0.6451 | 0.7752 | 48.0191 |
| 0.0545 | 6.14 | 15400 | 0.0778 | 0.7802 | 0.6464 | 0.7759 | 48.0191 |
| 0.079 | 6.22 | 15600 | 0.0781 | 0.7801 | 0.6466 | 0.7759 | 48.0191 |
| 0.0474 | 6.3 | 15800 | 0.0782 | 0.7809 | 0.6477 | 0.7768 | 48.0191 |
| 0.0517 | 6.38 | 16000 | 0.0788 | 0.7809 | 0.6481 | 0.7769 | 48.0191 |
| 0.0613 | 6.46 | 16200 | 0.0782 | 0.7814 | 0.6481 | 0.7773 | 48.0191 |
| 0.0517 | 6.54 | 16400 | 0.0785 | 0.7807 | 0.6468 | 0.7767 | 48.0191 |
| 0.0549 | 6.62 | 16600 | 0.0778 | 0.7817 | 0.6485 | 0.7777 | 48.0191 |
| 0.0727 | 6.7 | 16800 | 0.0774 | 0.7824 | 0.6493 | 0.7785 | 48.0191 |
| 0.0768 | 6.78 | 17000 | 0.0784 | 0.7826 | 0.6495 | 0.7785 | 48.0191 |
| 0.0612 | 6.86 | 17200 | 0.0772 | 0.7818 | 0.6485 | 0.7779 | 48.0191 |
| 0.0735 | 6.94 | 17400 | 0.0778 | 0.7817 | 0.6484 | 0.7777 | 48.0191 |
| 0.0662 | 7.02 | 17600 | 0.0780 | 0.7819 | 0.6483 | 0.7778 | 48.0191 |
| 0.0769 | 7.1 | 17800 | 0.0777 | 0.7823 | 0.6488 | 0.7784 | 48.0191 |
| 0.0649 | 7.18 | 18000 | 0.0775 | 0.7818 | 0.6482 | 0.7778 | 48.0191 |
| 0.0749 | 7.26 | 18200 | 0.0774 | 0.7822 | 0.6486 | 0.7781 | 48.0191 |
| 0.0568 | 7.34 | 18400 | 0.0772 | 0.7825 | 0.6488 | 0.7784 | 48.0191 |
| 0.0751 | 7.42 | 18600 | 0.0774 | 0.7822 | 0.6486 | 0.7783 | 48.0191 |
| 0.0564 | 7.5 | 18800 | 0.0773 | 0.7823 | 0.6487 | 0.7782 | 48.0191 |
| 0.0593 | 7.58 | 19000 | 0.0767 | 0.7826 | 0.6492 | 0.7786 | 48.0191 |
| 0.0563 | 7.66 | 19200 | 0.0773 | 0.7826 | 0.6497 | 0.7786 | 48.0191 |
| 0.0686 | 7.74 | 19400 | 0.0771 | 0.7828 | 0.6494 | 0.7789 | 48.0191 |
| 0.0728 | 7.82 | 19600 | 0.0772 | 0.7823 | 0.6494 | 0.7784 | 48.0191 |
| 0.06 | 7.9 | 19800 | 0.0772 | 0.7826 | 0.6491 | 0.7786 | 48.0191 |
| 0.0557 | 7.98 | 20000 | 0.0771 | 0.7828 | 0.6494 | 0.7787 | 48.0191 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.0
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