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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-pl30-2 |
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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-pl30-2 |
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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: 0.2998 |
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- Accuracy: 0.8997 |
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- Precision: 0.8804 |
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- Recall: 0.8766 |
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- F1: 0.8785 |
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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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| 0.5411 | 1.0 | 122 | 0.4939 | 0.7368 | 0.6762 | 0.6413 | 0.6509 | |
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| 0.4231 | 2.0 | 244 | 0.3852 | 0.8246 | 0.7888 | 0.8184 | 0.7995 | |
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| 0.3331 | 3.0 | 366 | 0.3313 | 0.8471 | 0.8233 | 0.7968 | 0.8081 | |
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| 0.2924 | 4.0 | 488 | 0.3057 | 0.8822 | 0.8610 | 0.8517 | 0.8561 | |
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| 0.2705 | 5.0 | 610 | 0.3069 | 0.8747 | 0.8605 | 0.8288 | 0.8422 | |
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| 0.2461 | 6.0 | 732 | 0.3119 | 0.8747 | 0.8436 | 0.8763 | 0.8562 | |
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| 0.2313 | 7.0 | 854 | 0.2880 | 0.8872 | 0.8606 | 0.8727 | 0.8662 | |
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| 0.2183 | 8.0 | 976 | 0.2773 | 0.8922 | 0.8749 | 0.8612 | 0.8676 | |
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| 0.2093 | 9.0 | 1098 | 0.2804 | 0.8847 | 0.8648 | 0.8534 | 0.8588 | |
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| 0.1986 | 10.0 | 1220 | 0.2890 | 0.8922 | 0.8804 | 0.8537 | 0.8655 | |
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| 0.1881 | 11.0 | 1342 | 0.2911 | 0.8872 | 0.8658 | 0.8602 | 0.8629 | |
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| 0.1802 | 12.0 | 1464 | 0.2866 | 0.8822 | 0.8596 | 0.8542 | 0.8568 | |
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| 0.169 | 13.0 | 1586 | 0.2964 | 0.8847 | 0.8697 | 0.8459 | 0.8565 | |
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| 0.1709 | 14.0 | 1708 | 0.2944 | 0.8872 | 0.8658 | 0.8602 | 0.8629 | |
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| 0.1492 | 15.0 | 1830 | 0.2866 | 0.8872 | 0.8645 | 0.8627 | 0.8636 | |
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| 0.1493 | 16.0 | 1952 | 0.2951 | 0.8947 | 0.8708 | 0.8780 | 0.8743 | |
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| 0.1425 | 17.0 | 2074 | 0.3048 | 0.8947 | 0.8773 | 0.8655 | 0.8711 | |
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| 0.1375 | 18.0 | 2196 | 0.2987 | 0.8997 | 0.8791 | 0.8791 | 0.8791 | |
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| 0.1326 | 19.0 | 2318 | 0.3073 | 0.8997 | 0.8819 | 0.8741 | 0.8778 | |
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| 0.1365 | 20.0 | 2440 | 0.2998 | 0.8997 | 0.8804 | 0.8766 | 0.8785 | |
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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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