---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-es
  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-finetuned-amazon-en-es

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: 3.0229
- Rouge1: 17.552
- Rouge2: 8.6159
- Rougel: 17.3207
- Rougelsum: 17.1968

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.6836        | 1.0   | 1209 | 3.2362          | 17.2827 | 8.6322 | 16.7811 | 16.7223   |
| 3.6489        | 2.0   | 2418 | 3.0808          | 17.7206 | 8.7236 | 17.0749 | 16.9989   |
| 3.4263        | 3.0   | 3627 | 3.0574          | 17.9532 | 9.55   | 17.604  | 17.4782   |
| 3.3129        | 4.0   | 4836 | 3.0444          | 16.8908 | 8.1947 | 16.3227 | 16.2468   |
| 3.2353        | 5.0   | 6045 | 3.0449          | 17.0334 | 8.1498 | 16.8367 | 16.6738   |
| 3.1678        | 6.0   | 7254 | 3.0326          | 18.197  | 9.3959 | 18.0328 | 17.86     |
| 3.1365        | 7.0   | 8463 | 3.0276          | 17.8769 | 9.1995 | 17.5326 | 17.4261   |
| 3.1118        | 8.0   | 9672 | 3.0229          | 17.552  | 8.6159 | 17.3207 | 17.1968   |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2