---
tags:
- generated_from_trainer
datasets:
- indosum
metrics:
- rouge
model-index:
- name: t5-base-indonesian-summarization-cased-finetuned-indosum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: indosum
      type: indosum
      config: indosum_fold0_source
      split: validation
      args: indosum_fold0_source
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3278
---

<!-- 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. -->

# t5-base-indonesian-summarization-cased-finetuned-indosum

This model is a fine-tuned version of [panggi/t5-base-indonesian-summarization-cased](https://huggingface.co/panggi/t5-base-indonesian-summarization-cased) on the indosum dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5884
- Rouge1: 0.3278
- Rouge2: 0.2868
- Rougel: 0.32
- Rougelsum: 0.3198
- Gen Len: 19.0

## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.3815        | 1.0   | 4754 | 0.5584          | 0.3281 | 0.2866 | 0.3202 | 0.32      | 19.0    |
| 0.3207        | 2.0   | 9508 | 0.5884          | 0.3278 | 0.2868 | 0.32   | 0.3198    | 19.0    |


### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3