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--- |
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library_name: transformers |
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license: mit |
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base_model: w11wo/indonesian-roberta-base-sentiment-classifier |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: results_final |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# results_final |
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This model is a fine-tuned version of [w11wo/indonesian-roberta-base-sentiment-classifier](https://huggingface.co/w11wo/indonesian-roberta-base-sentiment-classifier) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4292 |
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- Accuracy: 0.8881 |
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- F1 Macro: 0.8880 |
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- F1 Weighted: 0.8880 |
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- Precision Macro: 0.8896 |
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- Recall Macro: 0.8881 |
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- Precision Weighted: 0.8895 |
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- Recall Weighted: 0.8881 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted | Precision Macro | Recall Macro | Precision Weighted | Recall Weighted | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:-----------:|:---------------:|:------------:|:------------------:|:---------------:| |
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| 0.2316 | 0.3436 | 100 | 0.4071 | 0.8864 | 0.8862 | 0.8862 | 0.8869 | 0.8864 | 0.8870 | 0.8864 | |
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| 0.2298 | 0.6873 | 200 | 0.4028 | 0.8864 | 0.8863 | 0.8863 | 0.8867 | 0.8864 | 0.8867 | 0.8864 | |
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| 0.1311 | 1.0309 | 300 | 0.4144 | 0.8847 | 0.8846 | 0.8846 | 0.8852 | 0.8847 | 0.8852 | 0.8847 | |
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| 0.159 | 1.3746 | 400 | 0.4292 | 0.8881 | 0.8880 | 0.8880 | 0.8896 | 0.8881 | 0.8895 | 0.8881 | |
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| 0.1957 | 1.7182 | 500 | 0.4283 | 0.8830 | 0.8828 | 0.8829 | 0.8835 | 0.8829 | 0.8836 | 0.8830 | |
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| 0.228 | 2.0619 | 600 | 0.4153 | 0.8778 | 0.8778 | 0.8779 | 0.8783 | 0.8778 | 0.8783 | 0.8778 | |
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| 0.2248 | 2.4055 | 700 | 0.4242 | 0.8830 | 0.8828 | 0.8828 | 0.8833 | 0.8829 | 0.8833 | 0.8830 | |
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| 0.1733 | 2.7491 | 800 | 0.4239 | 0.8795 | 0.8795 | 0.8795 | 0.8803 | 0.8795 | 0.8803 | 0.8795 | |
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| 0.2314 | 3.0928 | 900 | 0.4166 | 0.8812 | 0.8811 | 0.8811 | 0.8813 | 0.8812 | 0.8813 | 0.8812 | |
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| 0.1691 | 3.4364 | 1000 | 0.4472 | 0.8744 | 0.8741 | 0.8741 | 0.8757 | 0.8743 | 0.8757 | 0.8744 | |
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| 0.2671 | 3.7801 | 1100 | 0.4273 | 0.8830 | 0.8828 | 0.8829 | 0.8832 | 0.8829 | 0.8832 | 0.8830 | |
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| 0.2643 | 4.1237 | 1200 | 0.4317 | 0.8812 | 0.8811 | 0.8811 | 0.8815 | 0.8812 | 0.8815 | 0.8812 | |
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| 0.202 | 4.4674 | 1300 | 0.4440 | 0.8847 | 0.8846 | 0.8846 | 0.8852 | 0.8846 | 0.8852 | 0.8847 | |
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| 0.2538 | 4.8110 | 1400 | 0.4397 | 0.8812 | 0.8812 | 0.8812 | 0.8816 | 0.8812 | 0.8816 | 0.8812 | |
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| 0.2662 | 5.1546 | 1500 | 0.4364 | 0.8847 | 0.8846 | 0.8846 | 0.8852 | 0.8846 | 0.8852 | 0.8847 | |
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| 0.2655 | 5.4983 | 1600 | 0.4298 | 0.8812 | 0.8812 | 0.8812 | 0.8816 | 0.8812 | 0.8816 | 0.8812 | |
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| 0.1933 | 5.8419 | 1700 | 0.4422 | 0.8847 | 0.8845 | 0.8845 | 0.8854 | 0.8846 | 0.8854 | 0.8847 | |
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| 0.2289 | 6.1856 | 1800 | 0.4282 | 0.8778 | 0.8776 | 0.8777 | 0.8778 | 0.8778 | 0.8778 | 0.8778 | |
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| 0.2298 | 6.5292 | 1900 | 0.4313 | 0.8795 | 0.8794 | 0.8794 | 0.8798 | 0.8795 | 0.8798 | 0.8795 | |
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| 0.2008 | 6.8729 | 2000 | 0.4344 | 0.8812 | 0.8811 | 0.8812 | 0.8816 | 0.8812 | 0.8817 | 0.8812 | |
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| 0.2107 | 7.2165 | 2100 | 0.4354 | 0.8830 | 0.8829 | 0.8829 | 0.8833 | 0.8829 | 0.8834 | 0.8830 | |
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| 0.2505 | 7.5601 | 2200 | 0.4353 | 0.8830 | 0.8829 | 0.8829 | 0.8833 | 0.8829 | 0.8834 | 0.8830 | |
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| 0.2134 | 7.9038 | 2300 | 0.4361 | 0.8830 | 0.8829 | 0.8829 | 0.8833 | 0.8829 | 0.8834 | 0.8830 | |
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| 0.2613 | 8.2474 | 2400 | 0.4344 | 0.8830 | 0.8828 | 0.8829 | 0.8832 | 0.8829 | 0.8832 | 0.8830 | |
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| 0.2128 | 8.5911 | 2500 | 0.4350 | 0.8830 | 0.8828 | 0.8829 | 0.8832 | 0.8829 | 0.8832 | 0.8830 | |
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| 0.212 | 8.9347 | 2600 | 0.4356 | 0.8830 | 0.8828 | 0.8829 | 0.8832 | 0.8829 | 0.8832 | 0.8830 | |
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| 0.2379 | 9.2784 | 2700 | 0.4359 | 0.8830 | 0.8828 | 0.8829 | 0.8832 | 0.8829 | 0.8832 | 0.8830 | |
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| 0.1471 | 9.6220 | 2800 | 0.4358 | 0.8830 | 0.8828 | 0.8829 | 0.8832 | 0.8829 | 0.8832 | 0.8830 | |
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| 0.1781 | 9.9656 | 2900 | 0.4359 | 0.8830 | 0.8828 | 0.8829 | 0.8832 | 0.8829 | 0.8832 | 0.8830 | |
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### Framework versions |
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- Transformers 4.51.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 2.14.4 |
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- Tokenizers 0.21.1 |
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