---
library_name: transformers
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-synthetic-data-plus-translated
  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-small-synthetic-data-plus-translated

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5891
- Rouge1: 0.6390
- Rouge2: 0.5109
- Rougel: 0.6157
- Rougelsum: 0.6175

## 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: 5.6e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 14.4747       | 1.0   | 100  | 4.4435          | 0.0225 | 0.0054 | 0.0205 | 0.0215    |
| 5.9023        | 2.0   | 200  | 1.9711          | 0.1865 | 0.0791 | 0.1562 | 0.1567    |
| 3.0374        | 3.0   | 300  | 1.3288          | 0.3668 | 0.2195 | 0.3565 | 0.3567    |
| 2.1905        | 4.0   | 400  | 1.1478          | 0.4430 | 0.2741 | 0.4186 | 0.4205    |
| 1.8996        | 5.0   | 500  | 1.0408          | 0.4754 | 0.3275 | 0.4564 | 0.4574    |
| 1.6959        | 6.0   | 600  | 0.9541          | 0.5463 | 0.3972 | 0.5258 | 0.5273    |
| 1.5593        | 7.0   | 700  | 0.8942          | 0.5594 | 0.4138 | 0.5406 | 0.5426    |
| 1.4334        | 8.0   | 800  | 0.8482          | 0.6064 | 0.4683 | 0.5855 | 0.5866    |
| 1.3929        | 9.0   | 900  | 0.8106          | 0.6130 | 0.4714 | 0.5895 | 0.5911    |
| 1.2918        | 10.0  | 1000 | 0.7851          | 0.6156 | 0.4770 | 0.5929 | 0.5935    |
| 1.2362        | 11.0  | 1100 | 0.7576          | 0.6270 | 0.4894 | 0.6054 | 0.6060    |
| 1.1781        | 12.0  | 1200 | 0.7402          | 0.6257 | 0.4867 | 0.6031 | 0.6042    |
| 1.1476        | 13.0  | 1300 | 0.7212          | 0.6221 | 0.4894 | 0.6018 | 0.6029    |
| 1.1052        | 14.0  | 1400 | 0.7064          | 0.6214 | 0.4873 | 0.5983 | 0.5995    |
| 1.0667        | 15.0  | 1500 | 0.6938          | 0.6300 | 0.4972 | 0.6073 | 0.6079    |
| 1.0421        | 16.0  | 1600 | 0.6855          | 0.6265 | 0.4952 | 0.6026 | 0.6036    |
| 1.0169        | 17.0  | 1700 | 0.6748          | 0.6244 | 0.4911 | 0.6021 | 0.6029    |
| 1.0036        | 18.0  | 1800 | 0.6599          | 0.6342 | 0.5087 | 0.6130 | 0.6142    |
| 0.9828        | 19.0  | 1900 | 0.6510          | 0.6349 | 0.5090 | 0.6136 | 0.6147    |
| 0.9589        | 20.0  | 2000 | 0.6471          | 0.6370 | 0.5074 | 0.6124 | 0.6135    |
| 0.9267        | 21.0  | 2100 | 0.6400          | 0.6345 | 0.5081 | 0.6117 | 0.6127    |
| 0.9361        | 22.0  | 2200 | 0.6318          | 0.6336 | 0.5066 | 0.6126 | 0.6140    |
| 0.8992        | 23.0  | 2300 | 0.6291          | 0.6346 | 0.5066 | 0.6122 | 0.6125    |
| 0.9029        | 24.0  | 2400 | 0.6224          | 0.6367 | 0.5103 | 0.6152 | 0.6166    |
| 0.8815        | 25.0  | 2500 | 0.6159          | 0.6374 | 0.5078 | 0.6141 | 0.6157    |
| 0.8914        | 26.0  | 2600 | 0.6133          | 0.6356 | 0.5109 | 0.6120 | 0.6138    |
| 0.8548        | 27.0  | 2700 | 0.6091          | 0.6371 | 0.5089 | 0.6125 | 0.6145    |
| 0.8683        | 28.0  | 2800 | 0.6047          | 0.6387 | 0.5131 | 0.6149 | 0.6169    |
| 0.8483        | 29.0  | 2900 | 0.6020          | 0.6368 | 0.5096 | 0.6121 | 0.6133    |
| 0.8409        | 30.0  | 3000 | 0.5996          | 0.6405 | 0.5118 | 0.6139 | 0.6159    |
| 0.8407        | 31.0  | 3100 | 0.5997          | 0.6398 | 0.5123 | 0.6159 | 0.6177    |
| 0.8338        | 32.0  | 3200 | 0.5970          | 0.6385 | 0.5096 | 0.6144 | 0.6164    |
| 0.801         | 33.0  | 3300 | 0.5947          | 0.6361 | 0.5078 | 0.6122 | 0.6141    |
| 0.833         | 34.0  | 3400 | 0.5941          | 0.6386 | 0.5111 | 0.6154 | 0.6172    |
| 0.7751        | 35.0  | 3500 | 0.5921          | 0.6368 | 0.5065 | 0.6129 | 0.6148    |
| 0.8281        | 36.0  | 3600 | 0.5906          | 0.6409 | 0.5125 | 0.6183 | 0.6199    |
| 0.7803        | 37.0  | 3700 | 0.5898          | 0.6377 | 0.5097 | 0.6143 | 0.6162    |
| 0.8139        | 38.0  | 3800 | 0.5896          | 0.6398 | 0.5116 | 0.6166 | 0.6185    |
| 0.7922        | 39.0  | 3900 | 0.5894          | 0.6388 | 0.5109 | 0.6156 | 0.6174    |
| 0.8269        | 40.0  | 4000 | 0.5891          | 0.6390 | 0.5109 | 0.6157 | 0.6175    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0