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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-r2a2d0.05-0 |
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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-r2a2d0.05-0 |
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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: 0.3642 |
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- Accuracy: 0.8346 |
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- Precision: 0.7993 |
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- Recall: 0.8080 |
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- F1: 0.8034 |
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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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| 0.5633 | 1.0 | 122 | 0.5100 | 0.7168 | 0.6536 | 0.6446 | 0.6484 | |
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| 0.5083 | 2.0 | 244 | 0.4999 | 0.7243 | 0.6825 | 0.7049 | 0.6887 | |
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| 0.4904 | 3.0 | 366 | 0.4595 | 0.7619 | 0.7120 | 0.7065 | 0.7091 | |
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| 0.4644 | 4.0 | 488 | 0.4287 | 0.7920 | 0.7520 | 0.7253 | 0.7358 | |
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| 0.4439 | 5.0 | 610 | 0.4399 | 0.7519 | 0.7127 | 0.7395 | 0.7203 | |
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| 0.4241 | 6.0 | 732 | 0.4027 | 0.8221 | 0.7860 | 0.7816 | 0.7837 | |
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| 0.4092 | 7.0 | 854 | 0.4019 | 0.8070 | 0.7674 | 0.7835 | 0.7743 | |
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| 0.3891 | 8.0 | 976 | 0.3805 | 0.8271 | 0.7912 | 0.7926 | 0.7919 | |
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| 0.3777 | 9.0 | 1098 | 0.3789 | 0.8271 | 0.7912 | 0.7926 | 0.7919 | |
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| 0.369 | 10.0 | 1220 | 0.3758 | 0.8396 | 0.8071 | 0.8040 | 0.8055 | |
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| 0.3531 | 11.0 | 1342 | 0.3805 | 0.8296 | 0.7933 | 0.8044 | 0.7984 | |
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| 0.3486 | 12.0 | 1464 | 0.3801 | 0.8321 | 0.7960 | 0.8112 | 0.8027 | |
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| 0.3472 | 13.0 | 1586 | 0.3675 | 0.8421 | 0.8098 | 0.8083 | 0.8091 | |
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| 0.3379 | 14.0 | 1708 | 0.3654 | 0.8371 | 0.8032 | 0.8047 | 0.8040 | |
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| 0.3353 | 15.0 | 1830 | 0.3703 | 0.8421 | 0.8080 | 0.8183 | 0.8127 | |
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| 0.3213 | 16.0 | 1952 | 0.3709 | 0.8371 | 0.8019 | 0.8147 | 0.8077 | |
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| 0.3214 | 17.0 | 2074 | 0.3641 | 0.8371 | 0.8024 | 0.8097 | 0.8059 | |
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| 0.3225 | 18.0 | 2196 | 0.3640 | 0.8371 | 0.8024 | 0.8097 | 0.8059 | |
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| 0.3159 | 19.0 | 2318 | 0.3649 | 0.8346 | 0.7993 | 0.8080 | 0.8034 | |
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| 0.3195 | 20.0 | 2440 | 0.3642 | 0.8346 | 0.7993 | 0.8080 | 0.8034 | |
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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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