distilbert-base-indonesian-finetuned-PRDECT-ID
This model is a fine-tuned version of
cahya/distilbert-base-indonesian 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.
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:
- num_train_epochs: 5
- per_device_train_batch_size: 16
- per_device_eval_batch_size: 16
- warmup_steps: 500
- weight_decay: 0.01
- logging_dir: ./logs
- logging_steps: 10
- eval_strategy: epoch
- save_strategy: epoch
Training and Evaluation Results
The following table summarizes the training and validation loss over the epochs:
| Epoch | Training Loss | Validation Loss |
|---|
| 1 | 0.000100 | 0.000062 |
| 2 | 0.000000 | 0.000038 |
| 3 | 0.000000 | 0.000025 |
| 4 | 0.000000 | 0.000017 |
| 5 | 0.000000 | 0.000014 |
Train output:
- global_step: 235
- training_loss: 3.9409913424219185e-05
- train_runtime: 44.6774
- train_samples_per_second: 83.04
- train_steps_per_second: 5.26
- total_flos: 122954683514880.0
- train_loss: 3.9409913424219185e-05
- epoch: 5.0
Evaluation:
- eval_loss: 1.3968576240586117e-05
- eval_runtime: 0.3321
- eval_samples_per_second: 270.973
- eval_steps_per_second: 18.065
- epoch: 5.0
Perplexity: 1.0000139686738017
These results indicate excellent model performance and generalization capabilities.
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
