---
license: mit
base_model: indobenchmark/indobert-base-p1
tags:
- generated_from_trainer
datasets:
- indonlu
metrics:
- accuracy
model-index:
- name: IndoBERT-Sentiment-Analysis
  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: 0.9452380952380952
language:
- id
- en
widget:
  - text: "Doi asik bgt orangnya"
  - example_title: "Example 1"
  - text: "Ada pengumuman nih gaiss, besok liburr"
  - example_title: "Example 2"
  - text: "Kok gitu sih kelakuannya"
  - example_title: "Example 3"
---

<!-- 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-Sentiment-Analysis

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: 0.4221
- Accuracy: 0.9452
- F1 Score: 0.9451

## 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: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- 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 | Accuracy | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| 0.3499        | 0.27  | 500  | 0.2392          | 0.9310   | 0.9311   |
| 0.3181        | 0.55  | 1000 | 0.3354          | 0.9175   | 0.9158   |
| 0.3001        | 0.82  | 1500 | 0.2965          | 0.9238   | 0.9243   |
| 0.2534        | 1.09  | 2000 | 0.3513          | 0.9222   | 0.9218   |
| 0.1692        | 1.36  | 2500 | 0.2657          | 0.9405   | 0.9399   |
| 0.1543        | 1.64  | 3000 | 0.4046          | 0.9198   | 0.9191   |
| 0.1827        | 1.91  | 3500 | 0.2800          | 0.9317   | 0.9319   |
| 0.1061        | 2.18  | 4000 | 0.3352          | 0.9389   | 0.9389   |
| 0.0639        | 2.45  | 4500 | 0.4033          | 0.9373   | 0.9365   |
| 0.0709        | 2.73  | 5000 | 0.3508          | 0.9365   | 0.9360   |
| 0.0922        | 3.0   | 5500 | 0.3313          | 0.9397   | 0.9394   |
| 0.0274        | 3.27  | 6000 | 0.3635          | 0.9444   | 0.9440   |
| 0.0273        | 3.54  | 6500 | 0.4074          | 0.9389   | 0.9387   |
| 0.0414        | 3.82  | 7000 | 0.3863          | 0.9405   | 0.9405   |
| 0.0156        | 4.09  | 7500 | 0.4128          | 0.9413   | 0.9412   |
| 0.0067        | 4.36  | 8000 | 0.4469          | 0.9397   | 0.9399   |
| 0.0056        | 4.63  | 8500 | 0.4297          | 0.9444   | 0.9445   |
| 0.0124        | 4.91  | 9000 | 0.4227          | 0.9452   | 0.9451   |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.0.dev20230729
- Datasets 2.14.0
- Tokenizers 0.15.2