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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-r8a1d0.1-0 |
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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-r8a1d0.1-0 |
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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: 0.3309 |
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- Accuracy: 0.8672 |
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- Precision: 0.8378 |
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- Recall: 0.8460 |
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- F1: 0.8417 |
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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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| 0.5622 | 1.0 | 122 | 0.5076 | 0.7193 | 0.6565 | 0.6464 | 0.6505 | |
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| 0.5003 | 2.0 | 244 | 0.4839 | 0.7469 | 0.7107 | 0.7409 | 0.7178 | |
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| 0.4614 | 3.0 | 366 | 0.4256 | 0.7719 | 0.7281 | 0.7436 | 0.7344 | |
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| 0.4022 | 4.0 | 488 | 0.3880 | 0.8170 | 0.7798 | 0.7756 | 0.7776 | |
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| 0.3678 | 5.0 | 610 | 0.4131 | 0.8020 | 0.7657 | 0.7974 | 0.7760 | |
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| 0.3376 | 6.0 | 732 | 0.3645 | 0.8321 | 0.7965 | 0.8037 | 0.7999 | |
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| 0.3268 | 7.0 | 854 | 0.3640 | 0.8346 | 0.7988 | 0.8180 | 0.8069 | |
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| 0.3044 | 8.0 | 976 | 0.3551 | 0.8346 | 0.7996 | 0.8055 | 0.8024 | |
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| 0.2984 | 9.0 | 1098 | 0.3509 | 0.8496 | 0.8169 | 0.8261 | 0.8212 | |
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| 0.2922 | 10.0 | 1220 | 0.3413 | 0.8521 | 0.8213 | 0.8229 | 0.8221 | |
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| 0.2666 | 11.0 | 1342 | 0.3494 | 0.8521 | 0.8193 | 0.8329 | 0.8254 | |
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| 0.2641 | 12.0 | 1464 | 0.3520 | 0.8546 | 0.8220 | 0.8372 | 0.8288 | |
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| 0.2694 | 13.0 | 1586 | 0.3358 | 0.8496 | 0.8202 | 0.8136 | 0.8167 | |
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| 0.2678 | 14.0 | 1708 | 0.3355 | 0.8647 | 0.8352 | 0.8417 | 0.8383 | |
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| 0.255 | 15.0 | 1830 | 0.3406 | 0.8647 | 0.8346 | 0.8442 | 0.8391 | |
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| 0.2482 | 16.0 | 1952 | 0.3370 | 0.8622 | 0.8309 | 0.8450 | 0.8373 | |
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| 0.2444 | 17.0 | 2074 | 0.3272 | 0.8697 | 0.8411 | 0.8478 | 0.8443 | |
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| 0.2521 | 18.0 | 2196 | 0.3319 | 0.8622 | 0.8314 | 0.8425 | 0.8365 | |
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| 0.2456 | 19.0 | 2318 | 0.3293 | 0.8697 | 0.8411 | 0.8478 | 0.8443 | |
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| 0.2458 | 20.0 | 2440 | 0.3309 | 0.8672 | 0.8378 | 0.8460 | 0.8417 | |
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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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