---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mT5-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-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: 0.0687
- Rouge1: 0.7322
- Rouge2: 0.5833
- Rougel: 0.7261
- Gen Len: 34.6325

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:-------:|
| 23.3756       | 0.48  | 200  | 16.6054         | 0.011  | 0.0006 | 0.0105 | 512.0   |
| 1.435         | 0.96  | 400  | 0.5767          | 0.1436 | 0.0387 | 0.1307 | 39.0358 |
| 0.2269        | 1.44  | 600  | 0.1658          | 0.4744 | 0.317  | 0.4631 | 34.6325 |
| 0.1709        | 1.91  | 800  | 0.1030          | 0.5865 | 0.4231 | 0.577  | 34.6325 |
| 0.1311        | 2.39  | 1000 | 0.0923          | 0.6245 | 0.4626 | 0.6173 | 34.6325 |
| 0.1196        | 2.87  | 1200 | 0.0851          | 0.6759 | 0.5159 | 0.6681 | 34.6325 |
| 0.1156        | 3.35  | 1400 | 0.0761          | 0.6927 | 0.5288 | 0.6845 | 34.6325 |
| 0.0897        | 3.83  | 1600 | 0.0756          | 0.698  | 0.5352 | 0.6906 | 34.6325 |
| 0.1149        | 4.31  | 1800 | 0.0738          | 0.7085 | 0.55   | 0.7022 | 34.6325 |
| 0.0967        | 4.78  | 2000 | 0.0725          | 0.7187 | 0.5632 | 0.712  | 34.6325 |
| 0.1097        | 5.26  | 2200 | 0.0703          | 0.7214 | 0.5645 | 0.7143 | 34.6325 |
| 0.0852        | 5.74  | 2400 | 0.0708          | 0.7241 | 0.5724 | 0.7176 | 34.6325 |
| 0.0537        | 6.22  | 2600 | 0.0693          | 0.7285 | 0.578  | 0.7217 | 34.6325 |
| 0.0866        | 6.7   | 2800 | 0.0698          | 0.7277 | 0.5795 | 0.7216 | 34.6325 |
| 0.0692        | 7.18  | 3000 | 0.0685          | 0.7336 | 0.5849 | 0.7274 | 34.6325 |
| 0.0786        | 7.66  | 3200 | 0.0687          | 0.7322 | 0.5833 | 0.7261 | 34.6325 |


### Framework versions

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