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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-r8a2d0.15-0 |
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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-r8a2d0.15-0 |
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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: 0.3217 |
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- Accuracy: 0.8622 |
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- Precision: 0.8326 |
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- Recall: 0.8375 |
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- F1: 0.8349 |
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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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| 0.5593 | 1.0 | 122 | 0.5026 | 0.7268 | 0.6658 | 0.6542 | 0.6589 | |
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| 0.4995 | 2.0 | 244 | 0.4797 | 0.7544 | 0.7149 | 0.7412 | 0.7226 | |
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| 0.4612 | 3.0 | 366 | 0.4282 | 0.7644 | 0.7199 | 0.7358 | 0.7262 | |
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| 0.4019 | 4.0 | 488 | 0.3934 | 0.8296 | 0.7949 | 0.7919 | 0.7934 | |
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| 0.3665 | 5.0 | 610 | 0.4234 | 0.7970 | 0.7618 | 0.7964 | 0.7720 | |
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| 0.334 | 6.0 | 732 | 0.3723 | 0.8195 | 0.7817 | 0.7973 | 0.7884 | |
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| 0.3263 | 7.0 | 854 | 0.3704 | 0.8346 | 0.7990 | 0.8230 | 0.8086 | |
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| 0.3076 | 8.0 | 976 | 0.3521 | 0.8471 | 0.8153 | 0.8168 | 0.8160 | |
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| 0.298 | 9.0 | 1098 | 0.3522 | 0.8471 | 0.8138 | 0.8243 | 0.8187 | |
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| 0.2923 | 10.0 | 1220 | 0.3375 | 0.8571 | 0.8289 | 0.8239 | 0.8264 | |
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| 0.2689 | 11.0 | 1342 | 0.3392 | 0.8622 | 0.8319 | 0.8400 | 0.8357 | |
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| 0.2686 | 12.0 | 1464 | 0.3484 | 0.8622 | 0.8309 | 0.8450 | 0.8373 | |
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| 0.2726 | 13.0 | 1586 | 0.3258 | 0.8596 | 0.8316 | 0.8282 | 0.8298 | |
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| 0.2713 | 14.0 | 1708 | 0.3246 | 0.8622 | 0.8333 | 0.8350 | 0.8341 | |
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| 0.2577 | 15.0 | 1830 | 0.3307 | 0.8596 | 0.8293 | 0.8357 | 0.8324 | |
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| 0.2519 | 16.0 | 1952 | 0.3305 | 0.8622 | 0.8314 | 0.8425 | 0.8365 | |
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| 0.2488 | 17.0 | 2074 | 0.3234 | 0.8546 | 0.8246 | 0.8246 | 0.8246 | |
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| 0.2546 | 18.0 | 2196 | 0.3247 | 0.8647 | 0.8346 | 0.8442 | 0.8391 | |
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| 0.2463 | 19.0 | 2318 | 0.3204 | 0.8596 | 0.8307 | 0.8307 | 0.8307 | |
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| 0.2458 | 20.0 | 2440 | 0.3217 | 0.8622 | 0.8326 | 0.8375 | 0.8349 | |
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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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