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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-pl30-0 |
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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-pl30-0 |
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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: 0.2913 |
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- Accuracy: 0.9048 |
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- Precision: 0.8851 |
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- Recall: 0.8851 |
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- F1: 0.8851 |
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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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| 0.5457 | 1.0 | 122 | 0.4753 | 0.7193 | 0.6465 | 0.5964 | 0.6014 | |
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| 0.4518 | 2.0 | 244 | 0.4070 | 0.7970 | 0.7589 | 0.7864 | 0.7685 | |
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| 0.3461 | 3.0 | 366 | 0.3412 | 0.8421 | 0.8231 | 0.7808 | 0.7970 | |
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| 0.2958 | 4.0 | 488 | 0.3253 | 0.8546 | 0.8263 | 0.8196 | 0.8229 | |
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| 0.2659 | 5.0 | 610 | 0.2941 | 0.8822 | 0.8610 | 0.8517 | 0.8561 | |
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| 0.2482 | 6.0 | 732 | 0.2965 | 0.8772 | 0.8473 | 0.8681 | 0.8563 | |
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| 0.2264 | 7.0 | 854 | 0.2869 | 0.8747 | 0.8447 | 0.8638 | 0.8531 | |
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| 0.2218 | 8.0 | 976 | 0.2795 | 0.8997 | 0.8961 | 0.8566 | 0.8730 | |
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| 0.2106 | 9.0 | 1098 | 0.2705 | 0.8922 | 0.8673 | 0.8763 | 0.8716 | |
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| 0.1981 | 10.0 | 1220 | 0.2751 | 0.9073 | 0.8920 | 0.8819 | 0.8867 | |
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| 0.1802 | 11.0 | 1342 | 0.2745 | 0.9048 | 0.8826 | 0.8901 | 0.8862 | |
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| 0.1828 | 12.0 | 1464 | 0.2799 | 0.9073 | 0.8957 | 0.8769 | 0.8855 | |
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| 0.1707 | 13.0 | 1586 | 0.2739 | 0.9098 | 0.8960 | 0.8837 | 0.8895 | |
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| 0.1606 | 14.0 | 1708 | 0.2868 | 0.9073 | 0.8862 | 0.8919 | 0.8890 | |
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| 0.1499 | 15.0 | 1830 | 0.2930 | 0.9023 | 0.8828 | 0.8808 | 0.8818 | |
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| 0.1555 | 16.0 | 1952 | 0.3041 | 0.8947 | 0.8682 | 0.8855 | 0.8760 | |
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| 0.1396 | 17.0 | 2074 | 0.2876 | 0.9023 | 0.8814 | 0.8833 | 0.8824 | |
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| 0.1477 | 18.0 | 2196 | 0.2900 | 0.9048 | 0.8865 | 0.8826 | 0.8845 | |
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| 0.1434 | 19.0 | 2318 | 0.2917 | 0.9048 | 0.8851 | 0.8851 | 0.8851 | |
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| 0.1386 | 20.0 | 2440 | 0.2913 | 0.9048 | 0.8851 | 0.8851 | 0.8851 | |
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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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