|
--- |
|
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 |
