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--- |
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language: |
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- id |
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license: mit |
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base_model: indolem/indobert-base-uncased |
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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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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: sentiment-lora-r2a2d0.1-1 |
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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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# sentiment-lora-r2a2d0.1-1 |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3681 |
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- Accuracy: 0.8396 |
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- Precision: 0.8141 |
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- Recall: 0.7865 |
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- F1: 0.7980 |
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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: 5e-05 |
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- train_batch_size: 30 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.566 | 1.0 | 122 | 0.5211 | 0.7168 | 0.6521 | 0.6396 | 0.6444 | |
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| 0.5148 | 2.0 | 244 | 0.5169 | 0.7243 | 0.6791 | 0.6974 | 0.6850 | |
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| 0.4927 | 3.0 | 366 | 0.4861 | 0.7544 | 0.7017 | 0.6887 | 0.6942 | |
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| 0.4627 | 4.0 | 488 | 0.4656 | 0.7619 | 0.7120 | 0.7065 | 0.7091 | |
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| 0.4504 | 5.0 | 610 | 0.4611 | 0.7544 | 0.7120 | 0.7337 | 0.7193 | |
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| 0.4276 | 6.0 | 732 | 0.4303 | 0.7895 | 0.7461 | 0.7410 | 0.7434 | |
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| 0.4176 | 7.0 | 854 | 0.4163 | 0.7945 | 0.7521 | 0.7546 | 0.7533 | |
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| 0.397 | 8.0 | 976 | 0.3960 | 0.8170 | 0.7814 | 0.7680 | 0.7741 | |
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| 0.3904 | 9.0 | 1098 | 0.3940 | 0.8271 | 0.7969 | 0.7726 | 0.7829 | |
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| 0.3743 | 10.0 | 1220 | 0.3900 | 0.8271 | 0.7994 | 0.7676 | 0.7804 | |
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| 0.3632 | 11.0 | 1342 | 0.3848 | 0.8346 | 0.8062 | 0.7830 | 0.7929 | |
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| 0.3599 | 12.0 | 1464 | 0.3795 | 0.8271 | 0.7959 | 0.7751 | 0.7841 | |
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| 0.3597 | 13.0 | 1586 | 0.3765 | 0.8346 | 0.8136 | 0.7705 | 0.7867 | |
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| 0.3461 | 14.0 | 1708 | 0.3729 | 0.8321 | 0.8061 | 0.7737 | 0.7867 | |
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| 0.3432 | 15.0 | 1830 | 0.3714 | 0.8371 | 0.8101 | 0.7847 | 0.7955 | |
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| 0.333 | 16.0 | 1952 | 0.3706 | 0.8421 | 0.8181 | 0.7883 | 0.8006 | |
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| 0.3323 | 17.0 | 2074 | 0.3700 | 0.8396 | 0.8155 | 0.7840 | 0.7969 | |
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| 0.3337 | 18.0 | 2196 | 0.3687 | 0.8396 | 0.8141 | 0.7865 | 0.7980 | |
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| 0.3298 | 19.0 | 2318 | 0.3684 | 0.8396 | 0.8141 | 0.7865 | 0.7980 | |
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| 0.3309 | 20.0 | 2440 | 0.3681 | 0.8396 | 0.8141 | 0.7865 | 0.7980 | |
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### Framework versions |
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- Transformers 4.39.3 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.15.2 |
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