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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-unipelt |
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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-unipelt |
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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: 0.2928 |
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- Accuracy: 0.9023 |
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- Precision: 0.8842 |
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- Recall: 0.8783 |
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- F1: 0.8812 |
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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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| 0.5535 | 1.0 | 122 | 0.4992 | 0.7293 | 0.6646 | 0.6285 | 0.6373 | |
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| 0.444 | 2.0 | 244 | 0.4053 | 0.8170 | 0.7847 | 0.8256 | 0.7961 | |
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| 0.3464 | 3.0 | 366 | 0.3425 | 0.8421 | 0.8345 | 0.7683 | 0.7905 | |
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| 0.2852 | 4.0 | 488 | 0.3136 | 0.8722 | 0.8445 | 0.8496 | 0.8470 | |
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| 0.2608 | 5.0 | 610 | 0.3060 | 0.8722 | 0.8445 | 0.8496 | 0.8470 | |
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| 0.2415 | 6.0 | 732 | 0.3100 | 0.8647 | 0.8325 | 0.8642 | 0.8447 | |
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| 0.2329 | 7.0 | 854 | 0.2860 | 0.8847 | 0.8567 | 0.8734 | 0.8642 | |
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| 0.199 | 8.0 | 976 | 0.2879 | 0.8872 | 0.8672 | 0.8577 | 0.8622 | |
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| 0.1939 | 9.0 | 1098 | 0.2826 | 0.8897 | 0.8659 | 0.8695 | 0.8676 | |
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| 0.1806 | 10.0 | 1220 | 0.2982 | 0.8797 | 0.8795 | 0.8224 | 0.8439 | |
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| 0.1674 | 11.0 | 1342 | 0.2735 | 0.8947 | 0.8730 | 0.8730 | 0.8730 | |
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| 0.1553 | 12.0 | 1464 | 0.2753 | 0.8947 | 0.8757 | 0.8680 | 0.8717 | |
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| 0.1431 | 13.0 | 1586 | 0.2937 | 0.8922 | 0.8785 | 0.8562 | 0.8662 | |
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| 0.1417 | 14.0 | 1708 | 0.2911 | 0.9073 | 0.8823 | 0.9019 | 0.8910 | |
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| 0.1236 | 15.0 | 1830 | 0.2956 | 0.9023 | 0.8828 | 0.8808 | 0.8818 | |
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| 0.1304 | 16.0 | 1952 | 0.3011 | 0.9023 | 0.8773 | 0.8933 | 0.8846 | |
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| 0.1164 | 17.0 | 2074 | 0.2943 | 0.8997 | 0.8778 | 0.8816 | 0.8797 | |
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| 0.1144 | 18.0 | 2196 | 0.2937 | 0.8972 | 0.8732 | 0.8823 | 0.8776 | |
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| 0.1198 | 19.0 | 2318 | 0.2985 | 0.8972 | 0.8812 | 0.8673 | 0.8738 | |
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| 0.1104 | 20.0 | 2440 | 0.2928 | 0.9023 | 0.8842 | 0.8783 | 0.8812 | |
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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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