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---
language:
- id
license: apache-2.0
base_model: LazarusNLP/IndoNanoT5-base
tags:
- generated_from_trainer
datasets:
- id_liputan6
metrics:
- rouge
model-index:
- name: liputan6-lora-16
results:
- task:
name: Summarization
type: summarization
dataset:
name: id_liputan6 canonical
type: id_liputan6
config: canonical
split: validation
args: canonical
metrics:
- name: Rouge1
type: rouge
value: 43.1279
---
<!-- 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. -->
# liputan6-lora-16
This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on the id_liputan6 canonical dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2652
- Rouge1: 43.1279
- Rouge2: 34.4893
- Rougel: 39.464
- Rougelsum: 41.6727
- Gen Len: 58.936
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.2779 | 1.0 | 63 | 0.3734 | 40.6247 | 32.4945 | 37.6262 | 39.2634 | 52.145 |
| 0.533 | 2.0 | 126 | 0.2652 | 42.9261 | 34.4419 | 39.4137 | 41.4698 | 53.098 |
| 0.4176 | 3.0 | 189 | 0.2285 | 40.0567 | 30.7942 | 36.765 | 38.66 | 50.993 |
| 0.364 | 4.0 | 252 | 0.2309 | 42.2149 | 33.065 | 38.5226 | 40.8353 | 55.49 |
| 0.3343 | 5.0 | 315 | 0.2211 | 41.3186 | 32.0318 | 37.7094 | 39.8931 | 54.221 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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