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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-pt-pl10-0 |
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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-pt-pl10-0 |
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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: 0.2766 |
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- Accuracy: 0.9023 |
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- Precision: 0.8802 |
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- Recall: 0.8858 |
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- F1: 0.8830 |
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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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| 0.5459 | 1.0 | 122 | 0.4763 | 0.7393 | 0.6804 | 0.6356 | 0.6459 | |
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| 0.4528 | 2.0 | 244 | 0.4306 | 0.7845 | 0.7630 | 0.8125 | 0.7678 | |
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| 0.3653 | 3.0 | 366 | 0.3335 | 0.8622 | 0.8533 | 0.8025 | 0.8217 | |
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| 0.2987 | 4.0 | 488 | 0.3357 | 0.8546 | 0.8246 | 0.8246 | 0.8246 | |
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| 0.2746 | 5.0 | 610 | 0.3401 | 0.8546 | 0.8217 | 0.8547 | 0.8339 | |
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| 0.2477 | 6.0 | 732 | 0.3323 | 0.8496 | 0.8176 | 0.8586 | 0.8308 | |
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| 0.24 | 7.0 | 854 | 0.3171 | 0.8647 | 0.8325 | 0.8642 | 0.8447 | |
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| 0.2069 | 8.0 | 976 | 0.2770 | 0.8922 | 0.8734 | 0.8637 | 0.8683 | |
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| 0.2197 | 9.0 | 1098 | 0.3091 | 0.8672 | 0.8356 | 0.8735 | 0.8491 | |
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| 0.2005 | 10.0 | 1220 | 0.2552 | 0.9023 | 0.8842 | 0.8783 | 0.8812 | |
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| 0.1867 | 11.0 | 1342 | 0.2727 | 0.9048 | 0.8816 | 0.8926 | 0.8868 | |
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| 0.1722 | 12.0 | 1464 | 0.2739 | 0.8922 | 0.8657 | 0.8813 | 0.8728 | |
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| 0.161 | 13.0 | 1586 | 0.2714 | 0.8997 | 0.8852 | 0.8691 | 0.8765 | |
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| 0.1684 | 14.0 | 1708 | 0.2774 | 0.8972 | 0.8723 | 0.8848 | 0.8781 | |
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| 0.1548 | 15.0 | 1830 | 0.2742 | 0.8997 | 0.8767 | 0.8841 | 0.8803 | |
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| 0.1526 | 16.0 | 1952 | 0.2970 | 0.8872 | 0.8574 | 0.8902 | 0.8703 | |
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| 0.1467 | 17.0 | 2074 | 0.2729 | 0.8897 | 0.8618 | 0.8820 | 0.8707 | |
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| 0.1484 | 18.0 | 2196 | 0.2739 | 0.8972 | 0.8723 | 0.8848 | 0.8781 | |
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| 0.1434 | 19.0 | 2318 | 0.2729 | 0.8997 | 0.8778 | 0.8816 | 0.8797 | |
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| 0.1354 | 20.0 | 2440 | 0.2766 | 0.9023 | 0.8802 | 0.8858 | 0.8830 | |
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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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