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--- |
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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-r4a2d0.15-0 |
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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-r4a2d0.15-0 |
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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: 0.3513 |
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- Accuracy: 0.8471 |
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- Precision: 0.8147 |
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- Recall: 0.8193 |
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- F1: 0.8169 |
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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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| 0.5621 | 1.0 | 122 | 0.5100 | 0.7218 | 0.6593 | 0.6482 | 0.6527 | |
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| 0.5049 | 2.0 | 244 | 0.4890 | 0.7343 | 0.6945 | 0.7195 | 0.7011 | |
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| 0.4776 | 3.0 | 366 | 0.4480 | 0.7594 | 0.7150 | 0.7323 | 0.7216 | |
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| 0.4422 | 4.0 | 488 | 0.4104 | 0.7945 | 0.7524 | 0.7446 | 0.7482 | |
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| 0.4146 | 5.0 | 610 | 0.4257 | 0.7594 | 0.7202 | 0.7473 | 0.7283 | |
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| 0.3828 | 6.0 | 732 | 0.3869 | 0.8246 | 0.7880 | 0.7909 | 0.7894 | |
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| 0.3697 | 7.0 | 854 | 0.3959 | 0.8145 | 0.7766 | 0.7988 | 0.7854 | |
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| 0.3486 | 8.0 | 976 | 0.3808 | 0.8321 | 0.7961 | 0.8087 | 0.8018 | |
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| 0.3437 | 9.0 | 1098 | 0.3738 | 0.8271 | 0.7904 | 0.8001 | 0.7949 | |
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| 0.3317 | 10.0 | 1220 | 0.3643 | 0.8471 | 0.8159 | 0.8143 | 0.8151 | |
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| 0.3114 | 11.0 | 1342 | 0.3683 | 0.8271 | 0.7902 | 0.8051 | 0.7968 | |
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| 0.3035 | 12.0 | 1464 | 0.3660 | 0.8346 | 0.7988 | 0.8155 | 0.8061 | |
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| 0.3117 | 13.0 | 1586 | 0.3518 | 0.8471 | 0.8167 | 0.8118 | 0.8142 | |
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| 0.3048 | 14.0 | 1708 | 0.3533 | 0.8446 | 0.8115 | 0.8176 | 0.8144 | |
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| 0.2916 | 15.0 | 1830 | 0.3570 | 0.8421 | 0.8083 | 0.8158 | 0.8119 | |
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| 0.2832 | 16.0 | 1952 | 0.3579 | 0.8471 | 0.8138 | 0.8243 | 0.8187 | |
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| 0.284 | 17.0 | 2074 | 0.3496 | 0.8471 | 0.8153 | 0.8168 | 0.8160 | |
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| 0.2906 | 18.0 | 2196 | 0.3537 | 0.8446 | 0.8111 | 0.8201 | 0.8153 | |
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| 0.2805 | 19.0 | 2318 | 0.3505 | 0.8496 | 0.8186 | 0.8186 | 0.8186 | |
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| 0.2815 | 20.0 | 2440 | 0.3513 | 0.8471 | 0.8147 | 0.8193 | 0.8169 | |
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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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