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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-r2a1d0.15-0 |
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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-r2a1d0.15-0 |
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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: 0.3672 |
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- Accuracy: 0.8321 |
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- Precision: 0.7961 |
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- Recall: 0.8087 |
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- F1: 0.8018 |
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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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| 0.563 | 1.0 | 122 | 0.5138 | 0.7243 | 0.6636 | 0.6549 | 0.6586 | |
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| 0.509 | 2.0 | 244 | 0.5057 | 0.7168 | 0.6763 | 0.6996 | 0.6820 | |
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| 0.4924 | 3.0 | 366 | 0.4708 | 0.7393 | 0.6877 | 0.6931 | 0.6901 | |
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| 0.468 | 4.0 | 488 | 0.4379 | 0.7845 | 0.7412 | 0.7200 | 0.7286 | |
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| 0.4495 | 5.0 | 610 | 0.4466 | 0.7594 | 0.7233 | 0.7548 | 0.7313 | |
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| 0.4334 | 6.0 | 732 | 0.4041 | 0.8271 | 0.7927 | 0.7851 | 0.7887 | |
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| 0.415 | 7.0 | 854 | 0.4057 | 0.7995 | 0.7590 | 0.7756 | 0.7660 | |
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| 0.3974 | 8.0 | 976 | 0.3852 | 0.8321 | 0.7982 | 0.7937 | 0.7959 | |
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| 0.3849 | 9.0 | 1098 | 0.3829 | 0.8246 | 0.7880 | 0.7909 | 0.7894 | |
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| 0.3771 | 10.0 | 1220 | 0.3786 | 0.8396 | 0.8065 | 0.8065 | 0.8065 | |
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| 0.3633 | 11.0 | 1342 | 0.3843 | 0.8296 | 0.7931 | 0.8069 | 0.7993 | |
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| 0.3591 | 12.0 | 1464 | 0.3833 | 0.8296 | 0.7931 | 0.8069 | 0.7993 | |
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| 0.354 | 13.0 | 1586 | 0.3705 | 0.8396 | 0.8065 | 0.8065 | 0.8065 | |
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| 0.3451 | 14.0 | 1708 | 0.3709 | 0.8371 | 0.8028 | 0.8072 | 0.8049 | |
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| 0.3403 | 15.0 | 1830 | 0.3733 | 0.8321 | 0.7960 | 0.8112 | 0.8027 | |
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| 0.3282 | 16.0 | 1952 | 0.3715 | 0.8346 | 0.7988 | 0.8155 | 0.8061 | |
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| 0.3286 | 17.0 | 2074 | 0.3664 | 0.8321 | 0.7965 | 0.8037 | 0.7999 | |
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| 0.3348 | 18.0 | 2196 | 0.3670 | 0.8271 | 0.7904 | 0.8001 | 0.7949 | |
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| 0.325 | 19.0 | 2318 | 0.3669 | 0.8321 | 0.7961 | 0.8087 | 0.8018 | |
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| 0.3266 | 20.0 | 2440 | 0.3672 | 0.8321 | 0.7961 | 0.8087 | 0.8018 | |
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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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