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1_model-for-sosmed-analysis-v3

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metadata
library_name: transformers
license: mit
base_model: w11wo/indonesian-roberta-base-sentiment-classifier
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: results_final
    results: []

results_final

This model is a fine-tuned version of w11wo/indonesian-roberta-base-sentiment-classifier on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4292
  • Accuracy: 0.8881
  • F1 Macro: 0.8880
  • F1 Weighted: 0.8880
  • Precision Macro: 0.8896
  • Recall Macro: 0.8881
  • Precision Weighted: 0.8895
  • Recall Weighted: 0.8881

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: 3e-06
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Macro F1 Weighted Precision Macro Recall Macro Precision Weighted Recall Weighted
0.2316 0.3436 100 0.4071 0.8864 0.8862 0.8862 0.8869 0.8864 0.8870 0.8864
0.2298 0.6873 200 0.4028 0.8864 0.8863 0.8863 0.8867 0.8864 0.8867 0.8864
0.1311 1.0309 300 0.4144 0.8847 0.8846 0.8846 0.8852 0.8847 0.8852 0.8847
0.159 1.3746 400 0.4292 0.8881 0.8880 0.8880 0.8896 0.8881 0.8895 0.8881
0.1957 1.7182 500 0.4283 0.8830 0.8828 0.8829 0.8835 0.8829 0.8836 0.8830
0.228 2.0619 600 0.4153 0.8778 0.8778 0.8779 0.8783 0.8778 0.8783 0.8778
0.2248 2.4055 700 0.4242 0.8830 0.8828 0.8828 0.8833 0.8829 0.8833 0.8830
0.1733 2.7491 800 0.4239 0.8795 0.8795 0.8795 0.8803 0.8795 0.8803 0.8795
0.2314 3.0928 900 0.4166 0.8812 0.8811 0.8811 0.8813 0.8812 0.8813 0.8812
0.1691 3.4364 1000 0.4472 0.8744 0.8741 0.8741 0.8757 0.8743 0.8757 0.8744
0.2671 3.7801 1100 0.4273 0.8830 0.8828 0.8829 0.8832 0.8829 0.8832 0.8830
0.2643 4.1237 1200 0.4317 0.8812 0.8811 0.8811 0.8815 0.8812 0.8815 0.8812
0.202 4.4674 1300 0.4440 0.8847 0.8846 0.8846 0.8852 0.8846 0.8852 0.8847
0.2538 4.8110 1400 0.4397 0.8812 0.8812 0.8812 0.8816 0.8812 0.8816 0.8812
0.2662 5.1546 1500 0.4364 0.8847 0.8846 0.8846 0.8852 0.8846 0.8852 0.8847
0.2655 5.4983 1600 0.4298 0.8812 0.8812 0.8812 0.8816 0.8812 0.8816 0.8812
0.1933 5.8419 1700 0.4422 0.8847 0.8845 0.8845 0.8854 0.8846 0.8854 0.8847
0.2289 6.1856 1800 0.4282 0.8778 0.8776 0.8777 0.8778 0.8778 0.8778 0.8778
0.2298 6.5292 1900 0.4313 0.8795 0.8794 0.8794 0.8798 0.8795 0.8798 0.8795
0.2008 6.8729 2000 0.4344 0.8812 0.8811 0.8812 0.8816 0.8812 0.8817 0.8812
0.2107 7.2165 2100 0.4354 0.8830 0.8829 0.8829 0.8833 0.8829 0.8834 0.8830
0.2505 7.5601 2200 0.4353 0.8830 0.8829 0.8829 0.8833 0.8829 0.8834 0.8830
0.2134 7.9038 2300 0.4361 0.8830 0.8829 0.8829 0.8833 0.8829 0.8834 0.8830
0.2613 8.2474 2400 0.4344 0.8830 0.8828 0.8829 0.8832 0.8829 0.8832 0.8830
0.2128 8.5911 2500 0.4350 0.8830 0.8828 0.8829 0.8832 0.8829 0.8832 0.8830
0.212 8.9347 2600 0.4356 0.8830 0.8828 0.8829 0.8832 0.8829 0.8832 0.8830
0.2379 9.2784 2700 0.4359 0.8830 0.8828 0.8829 0.8832 0.8829 0.8832 0.8830
0.1471 9.6220 2800 0.4358 0.8830 0.8828 0.8829 0.8832 0.8829 0.8832 0.8830
0.1781 9.9656 2900 0.4359 0.8830 0.8828 0.8829 0.8832 0.8829 0.8832 0.8830

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 2.14.4
  • Tokenizers 0.21.1