transformers tensorboard safetensors mt5 text2text-generation summarization generated_from_trainer base_model:google/mt5-small base_model:finetune:google/mt5-small license:apache-2.0 autotrain_compatible endpoints_compatible region:us
---
base_model: google/mt5-small
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: mt5-small-finetuned-indonesian-text-summarization-v3
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mt5-small-finetuned-indonesian-text-summarization-v3

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7499
- Rouge1: 63.8193
- Rouge2: 58.2187
- Rougel: 63.1123
- Rougelsum: 63.2782

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.2426        | 1.0   | 1783 | 0.8618          | 62.2558 | 56.8185 | 61.5786 | 61.6898   |
| 0.9017        | 2.0   | 3566 | 0.7946          | 62.8862 | 57.2359 | 62.151  | 62.3178   |
| 0.7717        | 3.0   | 5349 | 0.7633          | 63.2898 | 57.6478 | 62.552  | 62.7381   |
| 0.7093        | 4.0   | 7132 | 0.7402          | 63.6899 | 58.0258 | 62.9883 | 63.137    |
| 0.6815        | 5.0   | 8915 | 0.7499          | 63.8193 | 58.2187 | 63.1123 | 63.2782   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
