---
widget:
- text: >-
    Dih apaan banget dah buang sampah ke sungai begitu. Ada aktivis lingkungan
    yg sampe dipenjara karena menyuarakan peduli lingkungan. Ini pengangguran
    satu malah enak bener buang sampah sembarangan. Pantes lu susah, kelakuan lu
    nyusahin orang lain sih.
  example_title: Example 1
  output:
  - label: Disgust
    score: 0.672
  - label: Anger
    score: 0.282
  - label: Sadness
    score: 0.033
  - label: Joy
    score: 0.004
  - label: Surprise
    score: 0.003
  - label: Trust
    score: 0.003
  - label: Fear
    score: 0.002
  - label: Anticipation
    score: 0.001
- text: >-
    Februari 2009, wartawan Jawa Pos Radar Bali dibunuh dengan keji karena
    berita korupsi. Januari 2019, Presiden memberikan grasi kepada otak
    pembunuhan Prabangsa, dari seumur hidup menjadi cuma 20 tahun penjara.
    Sebuah langkah mundur yang menyakitkan!
  example_title: Example 2
  output:
  - label: Sadness
    score: 0.604
  - label: Anger
    score: 0.194
  - label: Surprise
    score: 0.127
  - label: Joy
    score: 0.021
  - label: Fear
    score: 0.018
  - label: Disgust
    score: 0.018
  - label: Anticipation
    score: 0.016
  - label: Trust
    score: 0.003
- text: >-
    Salut banget sama perjalanan hidup mereka ini kalo diproduksi jadi film
    pasti bakal rame dan menginspirasi banget woi
  example_title: Example 3
  output:
  - label: Joy
    score: 0.9637
  - label: Trust
    score: 0.0219
  - label: Anticipation
    score: 0.0079
  - label: Surprise
    score: 0.0029
  - label: Disgust
    score: 0.0013
  - label: Sadness
    score: 0.0010
  - label: Anger
    score: 0.0007
  - label: Fear
    score: 0.0006
- text: >-
    SUMPAH HARUS DIBEBASKAN!!! KENAPA GAK TANGKEPIN KORUPTOR AJA DARIPADA
    NGURUSIN MEME DARI AI GW MARAH BANGET SHIBAL
  example_title: Example 4
  output:
  - label: Anger
    score: 0.9889
  - label: Disgust
    score: 0.0035
  - label: Sadness
    score: 0.0026
  - label: Fear
    score: 0.0015
  - label: Surprise
    score: 0.0012
  - label: Trust
    score: 0.0011
  - label: Anticipation
    score: 0.0009
  - label: Joy
    score: 0.0003
- text: >-
    ga pernah pacaran, sekarang hidup kesepian bgt. pengen minta kenalin cowo
    ke temen tp mereka jg sama struggle nya. jd nyesel dulu pas sekolah-kuliah
    kenapa ga pernah 'macem2'
  example_title: Example 5
  output:
  - label: Sadness
    score: 0.9526
  - label: Anger
    score: 0.0175
  - label: Fear
    score: 0.0114
  - label: Disgust
    score: 0.0079
  - label: Trust
    score: 0.0038
  - label: Anticipation
    score: 0.0036
  - label: Joy
    score: 0.0019
  - label: Surprise
    score: 0.0013
- text: >-
    Komisi Penyiaran Indonesia (KPI) meminta agar tayangan televisi menampilkan
    citra positif Polri secara edukatif dan akurat. Hal ini disampaikan ketua
    KPI Pusat Ubaidillah dalam sebuah diskusi panel
  example_title: Example 6
  output:
  - label: Anticipation
    score: 0.4323
  - label: Trust
    score: 0.3996
  - label: Joy
    score: 0.0500
  - label: Anger
    score: 0.0388
  - label: Disgust
    score: 0.0362
  - label: Surprise
    score: 0.0186
  - label: Fear
    score: 0.0137
  - label: Sadness
    score: 0.0108

library_name: transformers
license: mit
language:
- id
---

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
The EmoSense-ID is a model designed to identify and analyze emotions in Indonesian texts based on Plutchik's eight basic emotions: Anticipation, Anger, Disgust, Fear, Joy, Sadness, Surprise, and Trust. 
This model is developed using the [NusaBERT-base](https://huggingface.co/LazarusNLP/NusaBERT-base)  and trained using Indonesian tweets categorized into eight emotion categories. The evaluation results of this model can be utilized to analyze emotions in social media, providing insights into users' emotional responses.

### Bias

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Keep in mind that this model is trained using certain data which may cause bias in the emotion classification process. Therefore, it is important to consider and account for such biases when using this model.

### Evaluation Results
The model was trained using the Hyperparameter Tuning technique with Optuna. In this process, Optuna conducted five trials to determine the optimal combination of learning rate (1e-6 to 1e-4) and weight decay (1e-6 to 1e-2). Each trial trained the BERT model with different hyperparameter configurations on the training dataset and then evaluated using the validation dataset. After all the experiments are completed, the best hyperparameter combination is used to train the final model. 

<table style="text-align: center; width: 100%;">
  <tr>
    <th>Epoch</th>
    <th>Training Loss</th>
    <th>Validation Loss</th>
    <th>Accuracy</th>
    <th>F1</th>
    <th>Precision</th>
    <th>Recall</th>
  </tr>
  <tr>
    <td>1</td>
    <td>0.758400</td>
    <td>0.583508</td>
    <td>0.829932</td>
    <td>0.830203</td>
    <td>0.833136</td>
    <td>0.829932</td>
  </tr>
  <tr>
    <td>2</td>
    <td>0.370100</td>
    <td>0.394630</td>
    <td>0.866213</td>
    <td>0.865496</td>
    <td>0.870364</td>
    <td>0.866213</td>
  </tr>
  <tr>
    <td>3</td>
    <td>0.231500</td>
    <td>0.355294</td>
    <td>0.884354</td>
    <td>0.884585</td>
    <td>0.888140</td>
    <td>0.884354</td>
  </tr>
  <tr>
    <td>4</td>
    <td>0.071000</td>
    <td>0.322376</td>
    <td>0.902494</td>
    <td>0.902801</td>
    <td>0.904842</td>
    <td>0.902494</td>
  </tr>
  <tr>
    <td>5</td>
    <td>0.129900</td>
    <td>0.308596</td>
    <td>0.900227</td>
    <td>0.900340</td>
    <td>0.902132</td>
    <td>0.900227</td>
  </tr>
</table>

## Citation

```
@misc{Ardiyanto_Mikhael_2024,
    author    = {Mikhael Ardiyanto},
    title     = {EmoSense-ID},
    year      = {2024},
    URL       = {Aardiiiiy/EmoSense-ID-Indonesian-Emotion-Classifier},
    publisher = {Hugging Face}
}