---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- wiki_lingua
metrics:
- rouge
model-index:
- name: wiki_lingua-en-8-3-5.6e-05-mt5-small-finetuned
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: wiki_lingua
      type: wiki_lingua
      config: en
      split: test
      args: en
    metrics:
    - name: Rouge1
      type: rouge
      value: 26.1973
---

<!-- 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. -->

# wiki_lingua-en-8-3-5.6e-05-mt5-small-finetuned

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wiki_lingua dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3335
- Rouge1: 26.1973
- Rouge2: 9.0993
- Rougel: 22.5436
- Rougelsum: 25.443

## Baseline Results
- Rouge1: 22.85
- Rouge2: 5.56
- Rougel: 15.13
- Rougelsum: 15.13

## 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: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.1095        | 1.0   | 11940 | 2.4383          | 24.7073 | 8.2191 | 21.4226 | 23.9708   |
| 2.7001        | 2.0   | 23880 | 2.3580          | 25.6263 | 8.7567 | 22.0851 | 24.8914   |
| 2.615         | 3.0   | 35820 | 2.3335          | 26.1973 | 9.0993 | 22.5436 | 25.443    |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2