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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-pl20-1 |
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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-pt-pl20-1 |
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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: 0.3296 |
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- Accuracy: 0.8872 |
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- Precision: 0.8624 |
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- Recall: 0.8677 |
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- F1: 0.8650 |
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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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| 0.5524 | 1.0 | 122 | 0.5143 | 0.7168 | 0.6417 | 0.5921 | 0.5963 | |
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| 0.468 | 2.0 | 244 | 0.4272 | 0.7920 | 0.7507 | 0.7678 | 0.7577 | |
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| 0.3759 | 3.0 | 366 | 0.3480 | 0.8346 | 0.8175 | 0.7655 | 0.7841 | |
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| 0.3116 | 4.0 | 488 | 0.3080 | 0.8647 | 0.8439 | 0.8217 | 0.8315 | |
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| 0.2812 | 5.0 | 610 | 0.3000 | 0.8697 | 0.8520 | 0.8253 | 0.8368 | |
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| 0.2692 | 6.0 | 732 | 0.2970 | 0.8772 | 0.8473 | 0.8681 | 0.8563 | |
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| 0.2603 | 7.0 | 854 | 0.2929 | 0.8772 | 0.8489 | 0.8606 | 0.8544 | |
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| 0.231 | 8.0 | 976 | 0.3083 | 0.8596 | 0.8486 | 0.8007 | 0.8190 | |
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| 0.2278 | 9.0 | 1098 | 0.2939 | 0.8697 | 0.8428 | 0.8428 | 0.8428 | |
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| 0.2117 | 10.0 | 1220 | 0.3240 | 0.8747 | 0.8647 | 0.8238 | 0.8404 | |
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| 0.2014 | 11.0 | 1342 | 0.2902 | 0.8797 | 0.8572 | 0.8499 | 0.8534 | |
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| 0.1869 | 12.0 | 1464 | 0.2760 | 0.8947 | 0.8698 | 0.8805 | 0.8749 | |
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| 0.1685 | 13.0 | 1586 | 0.3016 | 0.8822 | 0.8610 | 0.8517 | 0.8561 | |
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| 0.1703 | 14.0 | 1708 | 0.3027 | 0.8897 | 0.8632 | 0.8770 | 0.8695 | |
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| 0.1617 | 15.0 | 1830 | 0.3020 | 0.8897 | 0.8632 | 0.8770 | 0.8695 | |
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| 0.1524 | 16.0 | 1952 | 0.3177 | 0.8822 | 0.8530 | 0.8742 | 0.8622 | |
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| 0.1356 | 17.0 | 2074 | 0.3291 | 0.8897 | 0.8649 | 0.8720 | 0.8683 | |
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| 0.1474 | 18.0 | 2196 | 0.3268 | 0.8897 | 0.8649 | 0.8720 | 0.8683 | |
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| 0.145 | 19.0 | 2318 | 0.3315 | 0.8872 | 0.8614 | 0.8702 | 0.8656 | |
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| 0.1466 | 20.0 | 2440 | 0.3296 | 0.8872 | 0.8624 | 0.8677 | 0.8650 | |
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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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