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--- |
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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-pl30-1 |
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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-pl30-1 |
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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: 0.3019 |
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- Accuracy: 0.8647 |
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- Precision: 0.8377 |
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- Recall: 0.8342 |
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- F1: 0.8359 |
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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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| 0.5413 | 1.0 | 122 | 0.4949 | 0.7368 | 0.6763 | 0.6438 | 0.6531 | |
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| 0.4306 | 2.0 | 244 | 0.3954 | 0.8246 | 0.7902 | 0.8259 | 0.8019 | |
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| 0.3344 | 3.0 | 366 | 0.3397 | 0.8521 | 0.8370 | 0.7929 | 0.8099 | |
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| 0.2925 | 4.0 | 488 | 0.3211 | 0.8471 | 0.8264 | 0.7918 | 0.8058 | |
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| 0.2794 | 5.0 | 610 | 0.3064 | 0.8622 | 0.8314 | 0.8425 | 0.8365 | |
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| 0.2464 | 6.0 | 732 | 0.2857 | 0.8672 | 0.8356 | 0.8585 | 0.8453 | |
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| 0.2332 | 7.0 | 854 | 0.2846 | 0.8772 | 0.8496 | 0.8581 | 0.8537 | |
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| 0.2216 | 8.0 | 976 | 0.2906 | 0.8596 | 0.8360 | 0.8182 | 0.8262 | |
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| 0.2123 | 9.0 | 1098 | 0.2781 | 0.8697 | 0.8488 | 0.8303 | 0.8386 | |
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| 0.1911 | 10.0 | 1220 | 0.2896 | 0.8722 | 0.8562 | 0.8271 | 0.8395 | |
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| 0.1878 | 11.0 | 1342 | 0.2814 | 0.8747 | 0.8479 | 0.8513 | 0.8496 | |
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| 0.1797 | 12.0 | 1464 | 0.2830 | 0.8672 | 0.8402 | 0.8385 | 0.8394 | |
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| 0.1746 | 13.0 | 1586 | 0.2900 | 0.8672 | 0.8496 | 0.8210 | 0.8332 | |
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| 0.1677 | 14.0 | 1708 | 0.2798 | 0.8697 | 0.8411 | 0.8478 | 0.8443 | |
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| 0.1585 | 15.0 | 1830 | 0.2823 | 0.8722 | 0.8437 | 0.8521 | 0.8477 | |
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| 0.1575 | 16.0 | 1952 | 0.2816 | 0.8722 | 0.8413 | 0.8646 | 0.8511 | |
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| 0.146 | 17.0 | 2074 | 0.3027 | 0.8647 | 0.8377 | 0.8342 | 0.8359 | |
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| 0.1368 | 18.0 | 2196 | 0.2961 | 0.8672 | 0.8372 | 0.8485 | 0.8425 | |
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| 0.133 | 19.0 | 2318 | 0.3024 | 0.8622 | 0.8342 | 0.8325 | 0.8333 | |
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| 0.1377 | 20.0 | 2440 | 0.3019 | 0.8647 | 0.8377 | 0.8342 | 0.8359 | |
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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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