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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-pl30-4 |
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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-pl30-4 |
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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: 0.3168 |
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- Accuracy: 0.8797 |
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- Precision: 0.8530 |
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- Recall: 0.8599 |
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- F1: 0.8563 |
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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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| 0.5413 | 1.0 | 122 | 0.5007 | 0.7243 | 0.6593 | 0.6374 | 0.6446 | |
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| 0.4584 | 2.0 | 244 | 0.3856 | 0.8296 | 0.8123 | 0.7569 | 0.7761 | |
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| 0.3559 | 3.0 | 366 | 0.3407 | 0.8571 | 0.8638 | 0.7814 | 0.8079 | |
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| 0.2961 | 4.0 | 488 | 0.3089 | 0.8697 | 0.8438 | 0.8403 | 0.8420 | |
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| 0.276 | 5.0 | 610 | 0.2917 | 0.8622 | 0.8314 | 0.8425 | 0.8365 | |
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| 0.2555 | 6.0 | 732 | 0.2905 | 0.8697 | 0.8428 | 0.8428 | 0.8428 | |
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| 0.2427 | 7.0 | 854 | 0.3031 | 0.8772 | 0.8670 | 0.8281 | 0.8440 | |
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| 0.2219 | 8.0 | 976 | 0.2908 | 0.8772 | 0.8514 | 0.8531 | 0.8522 | |
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| 0.2158 | 9.0 | 1098 | 0.3084 | 0.8847 | 0.8760 | 0.8384 | 0.8540 | |
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| 0.2 | 10.0 | 1220 | 0.2938 | 0.8747 | 0.8457 | 0.8588 | 0.8517 | |
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| 0.1885 | 11.0 | 1342 | 0.2977 | 0.8772 | 0.8524 | 0.8506 | 0.8515 | |
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| 0.183 | 12.0 | 1464 | 0.3070 | 0.8847 | 0.8717 | 0.8434 | 0.8557 | |
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| 0.1752 | 13.0 | 1586 | 0.2959 | 0.8797 | 0.8522 | 0.8624 | 0.8570 | |
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| 0.1558 | 14.0 | 1708 | 0.3040 | 0.8747 | 0.8447 | 0.8638 | 0.8531 | |
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| 0.1538 | 15.0 | 1830 | 0.3082 | 0.8722 | 0.8431 | 0.8546 | 0.8484 | |
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| 0.152 | 16.0 | 1952 | 0.3100 | 0.8772 | 0.8576 | 0.8406 | 0.8484 | |
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| 0.1436 | 17.0 | 2074 | 0.3105 | 0.8747 | 0.8463 | 0.8563 | 0.8510 | |
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| 0.1426 | 18.0 | 2196 | 0.3119 | 0.8747 | 0.8471 | 0.8538 | 0.8503 | |
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| 0.1398 | 19.0 | 2318 | 0.3164 | 0.8797 | 0.8522 | 0.8624 | 0.8570 | |
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| 0.14 | 20.0 | 2440 | 0.3168 | 0.8797 | 0.8530 | 0.8599 | 0.8563 | |
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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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