---
license: mit
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
- f1
model-index:
- name: Fine-tuned-Indonesian-Sentiment-Classifier
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: indonlu
      type: indonlu
      config: smsa
      split: validation
      args: smsa
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9317460317460318
    - name: F1
      type: f1
      value: 0.9034223843742829
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. -->

# Fine-tuned-Indonesian-Sentiment-Classifier

This model is a fine-tuned version of [indobenchmark/indobert-base-p1](https://huggingface.co./indobenchmark/indobert-base-p1) on the indonlu dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3233
- Accuracy: 0.9317
- F1: 0.9034

## Model description

More information needed

## Intended uses & limitations

More information needed


### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.08          | 1.0   | 688   | 0.3532          | 0.9310   | 0.9053 |
| 0.0523        | 2.0   | 1376  | 0.3233          | 0.9317   | 0.9034 |
| 0.045         | 3.0   | 2064  | 0.3949          | 0.9286   | 0.8995 |
| 0.0252        | 4.0   | 2752  | 0.4662          | 0.9310   | 0.9049 |
| 0.0149        | 5.0   | 3440  | 0.6251          | 0.9246   | 0.8899 |
| 0.0091        | 6.0   | 4128  | 0.6148          | 0.9254   | 0.8928 |
| 0.0111        | 7.0   | 4816  | 0.6259          | 0.9222   | 0.8902 |
| 0.0106        | 8.0   | 5504  | 0.6123          | 0.9238   | 0.8882 |
| 0.0092        | 9.0   | 6192  | 0.6353          | 0.9230   | 0.8928 |
| 0.0085        | 10.0  | 6880  | 0.6733          | 0.9254   | 0.8989 |
| 0.0062        | 11.0  | 7568  | 0.6666          | 0.9302   | 0.9027 |
| 0.0036        | 12.0  | 8256  | 0.7578          | 0.9230   | 0.8962 |
| 0.0055        | 13.0  | 8944  | 0.7378          | 0.9270   | 0.8947 |
| 0.0023        | 14.0  | 9632  | 0.7758          | 0.9230   | 0.8978 |
| 0.0009        | 15.0  | 10320 | 0.7051          | 0.9278   | 0.9006 |
| 0.0033        | 16.0  | 11008 | 0.7442          | 0.9214   | 0.8902 |
| 0.0           | 17.0  | 11696 | 0.7513          | 0.9254   | 0.8974 |
| 0.0           | 18.0  | 12384 | 0.7554          | 0.9270   | 0.8999 |

Although trained with 18 epochs, this model uses the best weight (Epoch 2)

### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3