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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: LazarusNLP/IndoNanoT5-base-IndoSum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: indonlg
type: indonlg
config: indosum
split: test
args: indosum
metrics:
- name: Rouge1
type: rouge
value: 0.7529
- name: Rouge2
type: rouge
value: 0.7123
- name: RougeL
type: rouge
value: 0.733
---
<!-- 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-IndoSum
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: 0.1086
- Rouge1: 0.7529
- Rouge2: 0.7123
- Rougel: 0.733
- Rougelsum: 0.733
- Gen Len: 110.0391
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.3004 | 1.0 | 1761 | 0.1682 | 0.258 | 0.2277 | 0.2549 | 0.255 | 19.0 |
| 0.1463 | 2.0 | 3522 | 0.1318 | 0.2596 | 0.2305 | 0.2563 | 0.2565 | 19.0 |
| 0.095 | 3.0 | 5283 | 0.1272 | 0.2602 | 0.2314 | 0.2571 | 0.257 | 19.0 |
| 0.0705 | 4.0 | 7044 | 0.1186 | 0.2622 | 0.2338 | 0.2592 | 0.2592 | 19.0 |
| 0.0436 | 5.0 | 8805 | 0.1236 | 0.2625 | 0.2342 | 0.2594 | 0.2596 | 19.0 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.2.0+cu118
- Datasets 2.16.1
- Tokenizers 0.15.1
|