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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-r2a2d0.1-0 |
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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-r2a2d0.1-0 |
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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: 0.3608 |
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- Accuracy: 0.8471 |
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- Precision: 0.8138 |
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- Recall: 0.8243 |
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- F1: 0.8187 |
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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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| 0.5634 | 1.0 | 122 | 0.5108 | 0.7193 | 0.6572 | 0.6489 | 0.6524 | |
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| 0.5081 | 2.0 | 244 | 0.5049 | 0.7218 | 0.6829 | 0.7082 | 0.6888 | |
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| 0.4924 | 3.0 | 366 | 0.4667 | 0.7494 | 0.6977 | 0.6977 | 0.6977 | |
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| 0.4698 | 4.0 | 488 | 0.4392 | 0.7794 | 0.7349 | 0.7114 | 0.7207 | |
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| 0.4519 | 5.0 | 610 | 0.4548 | 0.7469 | 0.7169 | 0.7534 | 0.7226 | |
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| 0.4356 | 6.0 | 732 | 0.4111 | 0.8145 | 0.7770 | 0.7713 | 0.7740 | |
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| 0.421 | 7.0 | 854 | 0.4101 | 0.7945 | 0.7538 | 0.7721 | 0.7612 | |
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| 0.4039 | 8.0 | 976 | 0.3829 | 0.8296 | 0.7949 | 0.7919 | 0.7934 | |
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| 0.3887 | 9.0 | 1098 | 0.3800 | 0.8321 | 0.7972 | 0.7987 | 0.7979 | |
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| 0.3797 | 10.0 | 1220 | 0.3768 | 0.8371 | 0.8044 | 0.7997 | 0.8020 | |
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| 0.368 | 11.0 | 1342 | 0.3842 | 0.8221 | 0.7846 | 0.8016 | 0.7918 | |
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| 0.3598 | 12.0 | 1464 | 0.3778 | 0.8271 | 0.7902 | 0.8051 | 0.7968 | |
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| 0.3548 | 13.0 | 1586 | 0.3624 | 0.8471 | 0.8167 | 0.8118 | 0.8142 | |
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| 0.3469 | 14.0 | 1708 | 0.3637 | 0.8446 | 0.8120 | 0.8151 | 0.8135 | |
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| 0.3431 | 15.0 | 1830 | 0.3685 | 0.8396 | 0.8049 | 0.8165 | 0.8102 | |
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| 0.3275 | 16.0 | 1952 | 0.3664 | 0.8371 | 0.8017 | 0.8172 | 0.8086 | |
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| 0.3288 | 17.0 | 2074 | 0.3590 | 0.8396 | 0.8055 | 0.8115 | 0.8084 | |
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| 0.3335 | 18.0 | 2196 | 0.3607 | 0.8471 | 0.8138 | 0.8243 | 0.8187 | |
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| 0.3239 | 19.0 | 2318 | 0.3613 | 0.8446 | 0.8107 | 0.8226 | 0.8161 | |
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| 0.327 | 20.0 | 2440 | 0.3608 | 0.8471 | 0.8138 | 0.8243 | 0.8187 | |
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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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