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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-r8a2d0.05-0 |
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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-r8a2d0.05-0 |
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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: 0.3260 |
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- Accuracy: 0.8622 |
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- Precision: 0.8319 |
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- Recall: 0.8400 |
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- F1: 0.8357 |
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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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| 0.5609 | 1.0 | 122 | 0.5086 | 0.7193 | 0.6580 | 0.6514 | 0.6543 | |
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| 0.4986 | 2.0 | 244 | 0.4855 | 0.7494 | 0.7127 | 0.7427 | 0.7201 | |
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| 0.4593 | 3.0 | 366 | 0.4238 | 0.7694 | 0.7249 | 0.7394 | 0.7309 | |
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| 0.3957 | 4.0 | 488 | 0.3916 | 0.8070 | 0.7670 | 0.7735 | 0.7700 | |
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| 0.3658 | 5.0 | 610 | 0.4266 | 0.7995 | 0.7641 | 0.7981 | 0.7744 | |
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| 0.3345 | 6.0 | 732 | 0.3666 | 0.8371 | 0.8028 | 0.8072 | 0.8049 | |
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| 0.3237 | 7.0 | 854 | 0.3714 | 0.8396 | 0.8045 | 0.8265 | 0.8136 | |
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| 0.304 | 8.0 | 976 | 0.3537 | 0.8421 | 0.8083 | 0.8158 | 0.8119 | |
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| 0.3027 | 9.0 | 1098 | 0.3531 | 0.8446 | 0.8111 | 0.8201 | 0.8153 | |
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| 0.2962 | 10.0 | 1220 | 0.3382 | 0.8521 | 0.8220 | 0.8204 | 0.8212 | |
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| 0.2721 | 11.0 | 1342 | 0.3490 | 0.8496 | 0.8162 | 0.8311 | 0.8229 | |
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| 0.2693 | 12.0 | 1464 | 0.3502 | 0.8546 | 0.8220 | 0.8372 | 0.8288 | |
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| 0.2745 | 13.0 | 1586 | 0.3284 | 0.8571 | 0.8289 | 0.8239 | 0.8264 | |
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| 0.2712 | 14.0 | 1708 | 0.3297 | 0.8596 | 0.8299 | 0.8332 | 0.8315 | |
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| 0.256 | 15.0 | 1830 | 0.3357 | 0.8647 | 0.8346 | 0.8442 | 0.8391 | |
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| 0.2504 | 16.0 | 1952 | 0.3346 | 0.8571 | 0.8255 | 0.8364 | 0.8306 | |
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| 0.2487 | 17.0 | 2074 | 0.3242 | 0.8571 | 0.8281 | 0.8264 | 0.8272 | |
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| 0.2514 | 18.0 | 2196 | 0.3309 | 0.8622 | 0.8314 | 0.8425 | 0.8365 | |
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| 0.2451 | 19.0 | 2318 | 0.3243 | 0.8622 | 0.8333 | 0.8350 | 0.8341 | |
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| 0.2461 | 20.0 | 2440 | 0.3260 | 0.8622 | 0.8319 | 0.8400 | 0.8357 | |
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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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