---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- wiki_lingua
metrics:
- rouge
model-index:
- name: wiki_lingua-cs-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: cs
      split: test
      args: cs
    metrics:
    - name: Rouge1
      type: rouge
      value: 14.7738
---

<!-- 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-cs-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: 2.6706
- Rouge1: 14.7738
- Rouge2: 4.1406
- Rougel: 13.0515
- Rougelsum: 14.3388


# Baseline LEAD-64
- Rouge1: 21.28
- Rouge2: 4.55
- Rougel: 12.97
- Rougelsum: 12.98

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 5.8493        | 1.0   | 609  | 2.7949          | 12.8232 | 3.3348 | 11.0312 | 12.4584   |
| 3.701         | 2.0   | 1218 | 2.6966          | 14.6541 | 4.0724 | 12.9018 | 14.2196   |
| 3.546         | 3.0   | 1827 | 2.6706          | 14.7738 | 4.1406 | 13.0515 | 14.3388   |


### Framework versions

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