| --- |
| license: mit |
| base_model: ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: best_berita_bert_model_fold_5 |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>]() |
| # best_berita_bert_model_fold_5 |
| |
| This model is a fine-tuned version of [ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/bert-base-indonesian-1.5G-sentiment-analysis-smsa) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0859 |
| - Accuracy: 0.9833 |
| - Precision: 0.9834 |
| - Recall: 0.9830 |
| - F1: 0.9832 |
| |
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.5542 | 1.0 | 601 | 0.3531 | 0.9142 | 0.9204 | 0.9129 | 0.9108 | |
| | 0.266 | 2.0 | 1202 | 0.1554 | 0.9625 | 0.9634 | 0.9620 | 0.9618 | |
| | 0.1215 | 3.0 | 1803 | 0.0859 | 0.9833 | 0.9834 | 0.9830 | 0.9832 | |
| | 0.0721 | 4.0 | 2404 | 0.1634 | 0.9725 | 0.9736 | 0.9721 | 0.9720 | |
| | 0.0227 | 5.0 | 3005 | 0.4132 | 0.9484 | 0.9527 | 0.9475 | 0.9470 | |
| | 0.0242 | 6.0 | 3606 | 0.2816 | 0.9609 | 0.9632 | 0.9602 | 0.9599 | |
| | 0.0083 | 7.0 | 4207 | 0.2295 | 0.9717 | 0.9731 | 0.9712 | 0.9712 | |
| | 0.0 | 8.0 | 4808 | 0.1644 | 0.9792 | 0.9800 | 0.9788 | 0.9789 | |
| | 0.0002 | 9.0 | 5409 | 0.1868 | 0.9784 | 0.9792 | 0.9780 | 0.9781 | |
| | 0.0 | 10.0 | 6010 | 0.1901 | 0.9784 | 0.9792 | 0.9780 | 0.9781 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.42.3 |
| - Pytorch 2.1.2 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
| |
