---
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: 3.0318
- Rouge1: 0.1806
- Rouge2: 0.0917
- Rougel: 0.1765
- Rougelsum: 0.1766

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 6.6121        | 1.0   | 1209 | 3.2969          | 0.1522 | 0.0635 | 0.1476 | 0.147     |
| 3.901         | 2.0   | 2418 | 3.1307          | 0.1672 | 0.0821 | 0.1604 | 0.1602    |
| 3.5788        | 3.0   | 3627 | 3.0910          | 0.1804 | 0.0922 | 0.1748 | 0.1751    |
| 3.4198        | 4.0   | 4836 | 3.0646          | 0.1717 | 0.0813 | 0.167  | 0.1664    |
| 3.321         | 5.0   | 6045 | 3.0659          | 0.1782 | 0.0877 | 0.1759 | 0.1756    |
| 3.2441        | 6.0   | 7254 | 3.0407          | 0.1785 | 0.088  | 0.1755 | 0.1751    |
| 3.2075        | 7.0   | 8463 | 3.0356          | 0.1789 | 0.09   | 0.1743 | 0.1747    |
| 3.1803        | 8.0   | 9672 | 3.0318          | 0.1806 | 0.0917 | 0.1765 | 0.1766    |


### Framework versions

- Transformers 4.21.2
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1