---
license: apache-2.0
tags:
- generated_from_trainer
base_model: indolem/indobertweet-base-uncased
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: er-model
  results: []
datasets:
- SEACrowd/prdect_id
language:
- id
widget:
- text: Ini toko korup.,ga sesuai sama isinya..not recommended
  example_title: Contoh
---

<!-- 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. -->

# indobertweet-base-uncased-emotion-recognition



## Model description

This model is a fine-tuned version of [indolem/indobertweet-base-uncased](https://huggingface.co/indolem/indobertweet-base-uncased) on [The PRDECT-ID Dataset](https://www.kaggle.com/datasets/jocelyndumlao/prdect-id-indonesian-emotion-classification), it is a compilation of Indonesian product reviews that come with emotion and sentiment labels. These reviews were gathered from one of Indonesia's largest e-commerce platforms, Tokopedia.
It achieves the following results on the evaluation set:
- Loss: 0.6762
- Accuracy: 0.6981
- Precision: 0.7022
- Recall: 0.6981
- F1: 0.6963

It has been trained to classify text into six different emotion categories: happy, sadness, anger, love, and fear.


## Training and evaluation data

I split my dataframe df into training, validation, and testing sets (train_df, val_df, test_df) using the train_test_split function from sklearn.model_selection. I set the test size to 20% for the initial split and further divided the remaining data equally between validation and testing sets. This process ensures that each split (val_df and test_df) maintains the same class distribution as the original dataset (stratify=df['label']).



### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.7817        | 1.0   | 266  | 0.6859          | 0.7057   | 0.7140    | 0.7057 | 0.7061 |
| 0.6052        | 2.0   | 532  | 0.6762          | 0.6981   | 0.7022    | 0.6981 | 0.6963 |
| 0.488         | 3.0   | 798  | 0.7251          | 0.7189   | 0.7208    | 0.7189 | 0.7192 |
| 0.3578        | 4.0   | 1064 | 0.7943          | 0.7208   | 0.7240    | 0.7208 | 0.7222 |
| 0.2887        | 5.0   | 1330 | 0.8250          | 0.7038   | 0.7093    | 0.7038 | 0.7056 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1