transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-small
  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

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3524
- Rouge1: 21.18
- Rouge2: 6.37
- Rougel: 20.84

## 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: 9
- eval_batch_size: 9
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|
| 4.6211        | 1.45  | 500   | 2.5968          | 16.96  | 4.86   | 16.73  |
| 3.1269        | 2.9   | 1000  | 2.4790          | 17.62  | 5.0    | 17.58  |
| 2.884         | 4.35  | 1500  | 2.4077          | 17.67  | 5.06   | 17.4   |
| 2.7627        | 5.8   | 2000  | 2.4003          | 18.67  | 5.42   | 18.26  |
| 2.638         | 7.25  | 2500  | 2.3953          | 18.76  | 5.49   | 18.44  |
| 2.5427        | 8.7   | 3000  | 2.3837          | 18.97  | 6.04   | 18.62  |
| 2.4846        | 10.14 | 3500  | 2.3957          | 20.17  | 6.23   | 19.88  |
| 2.3867        | 11.59 | 4000  | 2.3558          | 19.5   | 6.24   | 19.1   |
| 2.3651        | 13.04 | 4500  | 2.3225          | 19.6   | 6.18   | 19.2   |
| 2.2846        | 14.49 | 5000  | 2.3385          | 19.34  | 6.3    | 18.9   |
| 2.2351        | 15.94 | 5500  | 2.3413          | 20.42  | 6.44   | 19.93  |
| 2.1862        | 17.39 | 6000  | 2.3418          | 20.04  | 6.35   | 19.51  |
| 2.1375        | 18.84 | 6500  | 2.3438          | 21.02  | 6.56   | 20.45  |
| 2.0961        | 20.29 | 7000  | 2.3451          | 20.82  | 6.81   | 20.6   |
| 2.0686        | 21.74 | 7500  | 2.3571          | 20.46  | 6.57   | 20.03  |
| 2.0253        | 23.19 | 8000  | 2.3672          | 20.49  | 6.21   | 20.16  |
| 1.9997        | 24.64 | 8500  | 2.3524          | 21.18  | 6.37   | 20.84  |
| 1.9627        | 26.09 | 9000  | 2.3780          | 20.9   | 5.96   | 20.4   |
| 1.9561        | 27.54 | 9500  | 2.3808          | 21.06  | 6.59   | 20.76  |
| 1.902         | 28.99 | 10000 | 2.3739          | 20.73  | 6.09   | 20.41  |
| 1.8837        | 30.43 | 10500 | 2.3786          | 20.65  | 6.27   | 20.35  |
| 1.8587        | 31.88 | 11000 | 2.3853          | 20.44  | 6.23   | 20.0   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
