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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-pl10-3 |
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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-pl10-3 |
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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: 0.3389 |
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- Accuracy: 0.8822 |
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- Precision: 0.8574 |
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- Recall: 0.8592 |
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- F1: 0.8583 |
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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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| 0.5509 | 1.0 | 122 | 0.4983 | 0.7393 | 0.6801 | 0.6406 | 0.6507 | |
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| 0.4511 | 2.0 | 244 | 0.4377 | 0.7769 | 0.7547 | 0.8022 | 0.7593 | |
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| 0.368 | 3.0 | 366 | 0.3260 | 0.8571 | 0.8381 | 0.8064 | 0.8196 | |
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| 0.3019 | 4.0 | 488 | 0.3036 | 0.8647 | 0.8410 | 0.8267 | 0.8333 | |
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| 0.2668 | 5.0 | 610 | 0.3192 | 0.8672 | 0.8372 | 0.8485 | 0.8425 | |
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| 0.2471 | 6.0 | 732 | 0.3059 | 0.8622 | 0.8305 | 0.8475 | 0.8380 | |
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| 0.2422 | 7.0 | 854 | 0.2950 | 0.8747 | 0.8451 | 0.8613 | 0.8524 | |
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| 0.2258 | 8.0 | 976 | 0.2928 | 0.8722 | 0.8463 | 0.8446 | 0.8454 | |
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| 0.2054 | 9.0 | 1098 | 0.3049 | 0.8797 | 0.8572 | 0.8499 | 0.8534 | |
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| 0.2009 | 10.0 | 1220 | 0.3013 | 0.8747 | 0.8488 | 0.8488 | 0.8488 | |
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| 0.1755 | 11.0 | 1342 | 0.3070 | 0.8822 | 0.8574 | 0.8592 | 0.8583 | |
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| 0.1821 | 12.0 | 1464 | 0.2995 | 0.8822 | 0.8596 | 0.8542 | 0.8568 | |
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| 0.1652 | 13.0 | 1586 | 0.3272 | 0.8847 | 0.8553 | 0.8809 | 0.8660 | |
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| 0.1566 | 14.0 | 1708 | 0.3336 | 0.8897 | 0.8609 | 0.8870 | 0.8719 | |
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| 0.1634 | 15.0 | 1830 | 0.3150 | 0.8847 | 0.8589 | 0.8659 | 0.8623 | |
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| 0.1496 | 16.0 | 1952 | 0.3321 | 0.8922 | 0.8706 | 0.8687 | 0.8697 | |
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| 0.1355 | 17.0 | 2074 | 0.3276 | 0.8847 | 0.8599 | 0.8634 | 0.8616 | |
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| 0.1477 | 18.0 | 2196 | 0.3365 | 0.8797 | 0.8530 | 0.8599 | 0.8563 | |
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| 0.1317 | 19.0 | 2318 | 0.3385 | 0.8822 | 0.8574 | 0.8592 | 0.8583 | |
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| 0.1267 | 20.0 | 2440 | 0.3389 | 0.8822 | 0.8574 | 0.8592 | 0.8583 | |
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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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