transformers tensorboard safetensors mt5 text2text-generation summarization generated_from_trainer base_model:google/mt5-small base_model:finetune:google/mt5-small license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: google/mt5-small
library_name: transformers
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-small-synthetic-data2
  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-data2

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: 0.8493
- Rouge1: 0.5864
- Rouge2: 0.4472
- Rougel: 0.5690
- Rougelsum: 0.5675

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 18.931        | 1.0   | 50   | 9.0942          | 0.0    | 0.0    | 0.0    | 0.0       |
| 11.3652       | 2.0   | 100  | 5.2268          | 0.0025 | 0.0014 | 0.0025 | 0.0025    |
| 7.6242        | 3.0   | 150  | 3.0583          | 0.0755 | 0.0247 | 0.0712 | 0.0697    |
| 5.0038        | 4.0   | 200  | 2.1559          | 0.1720 | 0.0608 | 0.1415 | 0.1411    |
| 3.4385        | 5.0   | 250  | 1.6094          | 0.2058 | 0.0858 | 0.1798 | 0.1801    |
| 2.7359        | 6.0   | 300  | 1.4043          | 0.3742 | 0.2353 | 0.3549 | 0.3574    |
| 2.2687        | 7.0   | 350  | 1.2929          | 0.4226 | 0.2639 | 0.3944 | 0.3962    |
| 2.0252        | 8.0   | 400  | 1.2258          | 0.4436 | 0.2820 | 0.4129 | 0.4159    |
| 1.8135        | 9.0   | 450  | 1.1667          | 0.4529 | 0.2932 | 0.4160 | 0.4176    |
| 1.7448        | 10.0  | 500  | 1.1103          | 0.4729 | 0.3152 | 0.4391 | 0.4409    |
| 1.5793        | 11.0  | 550  | 1.0840          | 0.5045 | 0.3557 | 0.4774 | 0.4787    |
| 1.5258        | 12.0  | 600  | 1.0532          | 0.5266 | 0.3857 | 0.5053 | 0.5061    |
| 1.4391        | 13.0  | 650  | 1.0176          | 0.5507 | 0.4182 | 0.5381 | 0.5367    |
| 1.3783        | 14.0  | 700  | 1.0015          | 0.5595 | 0.4233 | 0.5387 | 0.5386    |
| 1.318         | 15.0  | 750  | 0.9825          | 0.5699 | 0.4260 | 0.5476 | 0.5468    |
| 1.2871        | 16.0  | 800  | 0.9581          | 0.5785 | 0.4334 | 0.5564 | 0.5554    |
| 1.2305        | 17.0  | 850  | 0.9489          | 0.5766 | 0.4343 | 0.5540 | 0.5538    |
| 1.2609        | 18.0  | 900  | 0.9362          | 0.5853 | 0.4432 | 0.5633 | 0.5633    |
| 1.1928        | 19.0  | 950  | 0.9256          | 0.5847 | 0.4438 | 0.5637 | 0.5635    |
| 1.1165        | 20.0  | 1000 | 0.9186          | 0.5712 | 0.4331 | 0.5535 | 0.5535    |
| 1.1624        | 21.0  | 1050 | 0.9080          | 0.5763 | 0.4434 | 0.5581 | 0.5586    |
| 1.0909        | 22.0  | 1100 | 0.9040          | 0.5774 | 0.4417 | 0.5596 | 0.5604    |
| 1.0885        | 23.0  | 1150 | 0.8969          | 0.5827 | 0.4465 | 0.5642 | 0.5646    |
| 1.1378        | 24.0  | 1200 | 0.8933          | 0.5855 | 0.4476 | 0.5668 | 0.5663    |
| 0.9968        | 25.0  | 1250 | 0.8832          | 0.5851 | 0.4467 | 0.5664 | 0.5659    |
| 1.0871        | 26.0  | 1300 | 0.8776          | 0.5848 | 0.4460 | 0.5661 | 0.5659    |
| 1.0546        | 27.0  | 1350 | 0.8749          | 0.5825 | 0.4443 | 0.5635 | 0.5630    |
| 0.9935        | 28.0  | 1400 | 0.8687          | 0.5842 | 0.4467 | 0.5682 | 0.5678    |
| 1.0042        | 29.0  | 1450 | 0.8661          | 0.5834 | 0.4466 | 0.5669 | 0.5666    |
| 0.9903        | 30.0  | 1500 | 0.8628          | 0.5843 | 0.4485 | 0.5655 | 0.5653    |
| 0.9701        | 31.0  | 1550 | 0.8583          | 0.5822 | 0.4436 | 0.5630 | 0.5629    |
| 0.9585        | 32.0  | 1600 | 0.8552          | 0.5783 | 0.4405 | 0.5610 | 0.5605    |
| 0.9412        | 33.0  | 1650 | 0.8555          | 0.5897 | 0.4492 | 0.5696 | 0.5687    |
| 0.9732        | 34.0  | 1700 | 0.8526          | 0.5853 | 0.4477 | 0.5661 | 0.5655    |
| 0.9248        | 35.0  | 1750 | 0.8535          | 0.5828 | 0.4429 | 0.5646 | 0.5637    |
| 0.9408        | 36.0  | 1800 | 0.8520          | 0.5868 | 0.4474 | 0.5695 | 0.5680    |
| 0.9951        | 37.0  | 1850 | 0.8506          | 0.5834 | 0.4456 | 0.5656 | 0.5645    |
| 0.9316        | 38.0  | 1900 | 0.8500          | 0.5846 | 0.4470 | 0.5667 | 0.5657    |
| 0.9339        | 39.0  | 1950 | 0.8495          | 0.5864 | 0.4472 | 0.5690 | 0.5675    |
| 0.9519        | 40.0  | 2000 | 0.8493          | 0.5864 | 0.4472 | 0.5690 | 0.5675    |


### Framework versions

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