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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-pl50-2 |
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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-pl50-2 |
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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: 0.3235 |
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- Accuracy: 0.8747 |
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- Precision: 0.8537 |
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- Recall: 0.8388 |
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- F1: 0.8457 |
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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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| 0.5384 | 1.0 | 122 | 0.4919 | 0.7368 | 0.6770 | 0.6538 | 0.6617 | |
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| 0.4212 | 2.0 | 244 | 0.4175 | 0.8246 | 0.7930 | 0.8359 | 0.8048 | |
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| 0.3413 | 3.0 | 366 | 0.3403 | 0.8446 | 0.8257 | 0.7851 | 0.8009 | |
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| 0.2888 | 4.0 | 488 | 0.3278 | 0.8471 | 0.8159 | 0.8143 | 0.8151 | |
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| 0.2577 | 5.0 | 610 | 0.3103 | 0.8596 | 0.8325 | 0.8257 | 0.8290 | |
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| 0.2495 | 6.0 | 732 | 0.3074 | 0.8672 | 0.8436 | 0.8310 | 0.8369 | |
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| 0.2391 | 7.0 | 854 | 0.3005 | 0.8672 | 0.8402 | 0.8385 | 0.8394 | |
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| 0.2177 | 8.0 | 976 | 0.2979 | 0.8697 | 0.8449 | 0.8378 | 0.8412 | |
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| 0.2102 | 9.0 | 1098 | 0.2961 | 0.8797 | 0.8549 | 0.8549 | 0.8549 | |
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| 0.2029 | 10.0 | 1220 | 0.3043 | 0.8697 | 0.8579 | 0.8178 | 0.8340 | |
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| 0.1829 | 11.0 | 1342 | 0.3059 | 0.8797 | 0.8572 | 0.8499 | 0.8534 | |
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| 0.184 | 12.0 | 1464 | 0.3002 | 0.8772 | 0.8609 | 0.8356 | 0.8467 | |
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| 0.1802 | 13.0 | 1586 | 0.2954 | 0.8897 | 0.8710 | 0.8595 | 0.8649 | |
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| 0.1684 | 14.0 | 1708 | 0.3008 | 0.8872 | 0.8634 | 0.8652 | 0.8643 | |
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| 0.1627 | 15.0 | 1830 | 0.3067 | 0.8872 | 0.8672 | 0.8577 | 0.8622 | |
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| 0.1581 | 16.0 | 1952 | 0.3107 | 0.8772 | 0.8514 | 0.8531 | 0.8522 | |
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| 0.1468 | 17.0 | 2074 | 0.3229 | 0.8772 | 0.8576 | 0.8406 | 0.8484 | |
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| 0.1433 | 18.0 | 2196 | 0.3247 | 0.8747 | 0.8537 | 0.8388 | 0.8457 | |
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| 0.1538 | 19.0 | 2318 | 0.3246 | 0.8747 | 0.8537 | 0.8388 | 0.8457 | |
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| 0.1412 | 20.0 | 2440 | 0.3235 | 0.8747 | 0.8537 | 0.8388 | 0.8457 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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