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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-r2a2d0.05-1 |
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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-r2a2d0.05-1 |
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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: 0.3638 |
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- Accuracy: 0.8446 |
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- Precision: 0.8193 |
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- Recall: 0.7951 |
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- F1: 0.8055 |
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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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| 0.5663 | 1.0 | 122 | 0.5216 | 0.7293 | 0.6677 | 0.6510 | 0.6572 | |
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| 0.5149 | 2.0 | 244 | 0.5134 | 0.7243 | 0.6758 | 0.6899 | 0.6810 | |
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| 0.4925 | 3.0 | 366 | 0.4821 | 0.7569 | 0.7055 | 0.6980 | 0.7014 | |
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| 0.4608 | 4.0 | 488 | 0.4654 | 0.7644 | 0.7150 | 0.7083 | 0.7114 | |
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| 0.4493 | 5.0 | 610 | 0.4600 | 0.7569 | 0.7126 | 0.7305 | 0.7193 | |
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| 0.4257 | 6.0 | 732 | 0.4307 | 0.7870 | 0.7433 | 0.7318 | 0.7369 | |
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| 0.4178 | 7.0 | 854 | 0.4181 | 0.7970 | 0.7552 | 0.7614 | 0.7581 | |
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| 0.3977 | 8.0 | 976 | 0.3972 | 0.8070 | 0.7687 | 0.7560 | 0.7617 | |
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| 0.3946 | 9.0 | 1098 | 0.3937 | 0.8145 | 0.7779 | 0.7663 | 0.7716 | |
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| 0.3762 | 10.0 | 1220 | 0.3874 | 0.8246 | 0.7995 | 0.7584 | 0.7738 | |
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| 0.3727 | 11.0 | 1342 | 0.3787 | 0.8321 | 0.8014 | 0.7837 | 0.7915 | |
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| 0.3626 | 12.0 | 1464 | 0.3750 | 0.8371 | 0.8059 | 0.7947 | 0.7999 | |
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| 0.359 | 13.0 | 1586 | 0.3728 | 0.8296 | 0.8066 | 0.7644 | 0.7803 | |
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| 0.3488 | 14.0 | 1708 | 0.3709 | 0.8296 | 0.8049 | 0.7669 | 0.7816 | |
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| 0.3445 | 15.0 | 1830 | 0.3667 | 0.8421 | 0.8131 | 0.7983 | 0.8050 | |
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| 0.3344 | 16.0 | 1952 | 0.3656 | 0.8421 | 0.8142 | 0.7958 | 0.8040 | |
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| 0.3339 | 17.0 | 2074 | 0.3654 | 0.8396 | 0.8128 | 0.7890 | 0.7992 | |
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| 0.3357 | 18.0 | 2196 | 0.3638 | 0.8421 | 0.8154 | 0.7933 | 0.8029 | |
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| 0.3357 | 19.0 | 2318 | 0.3646 | 0.8421 | 0.8154 | 0.7933 | 0.8029 | |
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| 0.3359 | 20.0 | 2440 | 0.3638 | 0.8446 | 0.8193 | 0.7951 | 0.8055 | |
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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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