---
library_name: transformers
license: mit
base_model: indobenchmark/indobert-lite-base-p2
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: indobert-lite-base-p2
  results: []
datasets:
- indonlp/indonlu
- SEACrowd/id_google_play_review
language:
- id
pipeline_tag: text-classification
---

<!-- 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. -->

# indobert-lite-base-p2

This model is a fine-tuned version of [indobenchmark/indobert-lite-base-p2](https://huggingface.co/indobenchmark/indobert-lite-base-p2) on the indonlu-smsa and id_google_play_review dataset.
It achieves the following results on the evaluation set for combined dataset from indonlu-smsa and id_google_play_review:
- Loss: 0.4257
- Accuracy: 0.9291
- Precision: 0.8637
- Recall: 0.8651
- F1: 0.8643

Seperate evaluation
indonlu/indonlu-smsa
- Accuracy: 0.9269
- Precision: 0.9067
- Recall: 0.8948
- F1: 0.89995

## Model description

https://huggingface.co/indobenchmark/indobert-lite-base-p2

To Do:
- Add optimized model from optimum

## Intended uses & limitations

Sentiment Analysis, this model more lightweight than bert base and roberta base
ofc because this is lite model haha

## Training and evaluation data

The training combined all training data from indonlu-smsa and id google play review
The evaluation is conducted using the validation split

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0744        | 1.0   | 1127 | 0.3146          | 0.9309   | 0.8846    | 0.8549 | 0.8691 |
| 0.056         | 2.0   | 2254 | 0.3321          | 0.9298   | 0.8737    | 0.8580 | 0.8650 |
| 0.0299        | 3.0   | 3381 | 0.3906          | 0.9316   | 0.8750    | 0.8656 | 0.8702 |
| 0.0317        | 4.0   | 4508 | 0.4257          | 0.9291   | 0.8637    | 0.8651 | 0.8643 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.0