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---
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
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