---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- wiki_lingua
metrics:
- rouge
model-index:
- name: wiki_lingua-de-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: de
      split: test
      args: de
    metrics:
    - name: Rouge1
      type: rouge
      value: 15.2299
language:
- de
---

<!-- 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-de-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: 2.4218
- Rouge1: 15.2299
- Rouge2: 4.4912
- Rougel: 13.4991
- Rougelsum: 14.9193


# Baseline LEAD64
- Rouge1: 18.76
- Rouge2: 4.22
- Rougel: 12.14
- Rougelsum: 12.14

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 3.5656        | 1.0   | 4939  | 2.5421          | 14.4738 | 4.064  | 12.7061 | 14.1813   |
| 2.9444        | 2.0   | 9878  | 2.4492          | 14.8349 | 4.3457 | 13.16   | 14.5623   |
| 2.8378        | 3.0   | 14817 | 2.4218          | 15.2299 | 4.4912 | 13.4991 | 14.9193   |


### Framework versions

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