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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-seq_bn-rf16
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: 27.6391
---
<!-- 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-seq_bn-rf16
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: 2.7479
- Rouge1: 27.6391
- Rouge2: 12.5407
- Rougel: 23.5774
- Rougelsum: 25.3376
- Gen Len: 39.933
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.6241 | 1.0 | 63 | 2.7534 | 23.3287 | 9.5988 | 20.1923 | 21.2916 | 33.387 |
| 2.228 | 2.0 | 126 | 2.7025 | 25.8033 | 10.8168 | 21.9451 | 23.4491 | 32.153 |
| 2.0615 | 3.0 | 189 | 2.6749 | 25.8887 | 10.7586 | 22.113 | 23.8997 | 30.873 |
| 1.9099 | 4.0 | 252 | 2.7197 | 26.5565 | 11.2255 | 22.6026 | 24.5495 | 31.524 |
| 1.8007 | 5.0 | 315 | 2.7479 | 26.9743 | 11.4843 | 22.9863 | 24.9284 | 33.854 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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