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-8-3-5.6e-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: 0.4632
      name: Rouge1
---

<!-- 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-ar-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: 2.5040
- Rouge1: 0.4632
- Rouge2: 0.1185
- Rougel: 0.4644
- Rougelsum: 0.47

## Baseline Results
- Rouge1: 1.55
- Rouge2: 0.48
- Rougel: 1.51

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.981         | 1.0   | 2499 | 2.6174          | 0.4085 | 0.1027 | 0.4062 | 0.4141    |
| 3.0985        | 2.0   | 4998 | 2.5261          | 0.5131 | 0.1541 | 0.5113 | 0.5154    |
| 2.9914        | 3.0   | 7497 | 2.5040          | 0.4632 | 0.1185 | 0.4644 | 0.47      |


### Framework versions

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