transformers pytorch tensorboard mt5 text2text-generation summarization generated_from_trainer dataset:wiki_lingua license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- wiki_lingua
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: wiki_lingua-ar-8-8-5.6e-05-mt5-small-finetuned
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: wiki_lingua
      type: wiki_lingua
      config: ar
      split: test
      args: ar
    metrics:
    - type: rouge
      value: 0.5417
      name: Rouge1
---

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# wiki_lingua-ar-8-8-5.6e-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: 2.3401
- Rouge1: 0.5417
- Rouge2: 0.0921
- Rougel: 0.5445
- Rougelsum: 0.5404

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.851         | 1.0   | 2499  | 2.5949          | 0.4653 | 0.1378 | 0.4684 | 0.4631    |
| 3.0409        | 2.0   | 4998  | 2.4790          | 0.4825 | 0.1156 | 0.4834 | 0.4798    |
| 2.8824        | 3.0   | 7497  | 2.4273          | 0.5264 | 0.1331 | 0.5307 | 0.522     |
| 2.7853        | 4.0   | 9996  | 2.3945          | 0.4879 | 0.1191 | 0.4871 | 0.4811    |
| 2.7221        | 5.0   | 12495 | 2.3678          | 0.5655 | 0.0981 | 0.5672 | 0.5595    |
| 2.6797        | 6.0   | 14994 | 2.3511          | 0.5002 | 0.1191 | 0.5084 | 0.4968    |
| 2.6516        | 7.0   | 17493 | 2.3409          | 0.5606 | 0.1138 | 0.5631 | 0.5578    |
| 2.6364        | 8.0   | 19992 | 2.3401          | 0.5417 | 0.0921 | 0.5445 | 0.5404    |


### Framework versions

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