---
license: apache-2.0
base_model: LazarusNLP/IndoNanoT5-base
tags:
- generated_from_trainer
language:
- ind
datasets:
- GEM/indonlg
metrics:
- rouge
model-index:
- name: IndoNanoT5-base-Liputan6-Canonical
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: indonlg
      type: indonlg
      config: liputan6_canonical
      split: test
      args: liputan6_canonical
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3976
    - name: Rouge2
      type: rouge
      value: 0.2229
    - name: RougeL
      type: rouge
      value: 0.3346
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: indonlg
      type: indonlg
      config: liputan6_extreme
      split: test
      args: liputan6_extreme
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3323
    - name: Rouge2
      type: rouge
      value: 0.1417
    - name: RougeL
      type: rouge
      value: 0.2621
---

<!-- 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. -->

# LazarusNLP/IndoNanoT5-base-Liputan6-Canonical

This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on the indonlg dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1194
- Rouge1: 0.3976
- Rouge2: 0.2229
- Rougel: 0.3346
- Rougelsum: 0.3345
- Gen Len: 43.3808

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.9693        | 1.0   | 24236  | 1.3245          | 0.3082 | 0.1585 | 0.2687 | 0.2688    | 18.9956 |
| 0.9338        | 2.0   | 48472  | 1.2759          | 0.3105 | 0.159  | 0.2705 | 0.2706    | 18.9985 |
| 0.8632        | 3.0   | 72708  | 1.2698          | 0.3094 | 0.1586 | 0.2701 | 0.2702    | 18.9995 |
| 0.8257        | 4.0   | 96944  | 1.2631          | 0.312  | 0.1603 | 0.2716 | 0.2715    | 18.9993 |
| 0.7789        | 5.0   | 121180 | 1.2642          | 0.3149 | 0.1625 | 0.2748 | 0.2747    | 18.9998 |
| 0.7595        | 6.0   | 145416 | 1.2587          | 0.3202 | 0.1658 | 0.279  | 0.2791    | 18.9995 |
| 0.7343        | 7.0   | 169652 | 1.2644          | 0.3183 | 0.1647 | 0.2773 | 0.2773    | 18.9996 |
| 0.7165        | 8.0   | 193888 | 1.2635          | 0.3141 | 0.1605 | 0.2732 | 0.2732    | 18.9993 |
| 0.6697        | 9.0   | 218124 | 1.2856          | 0.316  | 0.162  | 0.275  | 0.275     | 18.9998 |
| 0.6729        | 10.0  | 242360 | 1.2809          | 0.3195 | 0.164  | 0.2775 | 0.2776    | 18.9992 |
| 0.6471        | 11.0  | 266596 | 1.2833          | 0.3185 | 0.1636 | 0.2769 | 0.277     | 18.9982 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1