| --- |
| license: mit |
| base_model: ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: best_berita_roberta_model_fold_2 |
| 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_roberta_model_fold_2 |
| |
| This model is a fine-tuned version of [ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa](https://huggingface.co/ayameRushia/roberta-base-indonesian-sentiment-analysis-smsa) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1019 |
| - Accuracy: 0.9884 |
| - Precision: 0.9882 |
| - Recall: 0.9889 |
| - F1: 0.9884 |
| |
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.6027 | 1.0 | 601 | 0.4467 | 0.8943 | 0.9036 | 0.8947 | 0.8951 | |
| | 0.2296 | 2.0 | 1202 | 0.3961 | 0.9218 | 0.9282 | 0.9262 | 0.9213 | |
| | 0.1253 | 3.0 | 1803 | 0.2066 | 0.9651 | 0.9655 | 0.9670 | 0.9652 | |
| | 0.0869 | 4.0 | 2404 | 0.1721 | 0.9684 | 0.9691 | 0.9699 | 0.9688 | |
| | 0.0188 | 5.0 | 3005 | 0.1239 | 0.9842 | 0.9840 | 0.9850 | 0.9843 | |
| | 0.0049 | 6.0 | 3606 | 0.1186 | 0.9825 | 0.9823 | 0.9835 | 0.9826 | |
| | 0.0057 | 7.0 | 4207 | 0.1019 | 0.9884 | 0.9882 | 0.9889 | 0.9884 | |
| | 0.0043 | 8.0 | 4808 | 0.3787 | 0.9534 | 0.9549 | 0.9562 | 0.9534 | |
| | 0.0047 | 9.0 | 5409 | 0.2094 | 0.9759 | 0.9759 | 0.9772 | 0.9760 | |
| | 0.0007 | 10.0 | 6010 | 0.2093 | 0.9759 | 0.9759 | 0.9772 | 0.9760 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.42.3 |
| - Pytorch 2.1.2 |
| - Datasets 2.20.0 |
| - Tokenizers 0.19.1 |
| |
