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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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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: nerugm-base-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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# nerugm-base-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: 0.2607 |
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- Precision: 0.8198 |
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- Recall: 0.8946 |
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- F1: 0.8556 |
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- Accuracy: 0.9651 |
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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: 16 |
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- eval_batch_size: 64 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.3182 | 1.0 | 106 | 0.1284 | 0.7463 | 0.8547 | 0.7968 | 0.9572 | |
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| 0.1137 | 2.0 | 212 | 0.1302 | 0.7230 | 0.8775 | 0.7928 | 0.9562 | |
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| 0.0683 | 3.0 | 318 | 0.1249 | 0.7833 | 0.8547 | 0.8174 | 0.9606 | |
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| 0.0454 | 4.0 | 424 | 0.1464 | 0.7711 | 0.8832 | 0.8234 | 0.9591 | |
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| 0.0325 | 5.0 | 530 | 0.1557 | 0.8010 | 0.8832 | 0.8401 | 0.9641 | |
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| 0.0211 | 6.0 | 636 | 0.2112 | 0.7915 | 0.8974 | 0.8411 | 0.9599 | |
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| 0.015 | 7.0 | 742 | 0.1944 | 0.7734 | 0.8946 | 0.8296 | 0.9606 | |
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| 0.0113 | 8.0 | 848 | 0.2151 | 0.8140 | 0.8974 | 0.8537 | 0.9665 | |
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| 0.0075 | 9.0 | 954 | 0.1996 | 0.8140 | 0.8974 | 0.8537 | 0.9685 | |
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| 0.0067 | 10.0 | 1060 | 0.2077 | 0.8470 | 0.8832 | 0.8647 | 0.9685 | |
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| 0.0039 | 11.0 | 1166 | 0.2609 | 0.7698 | 0.8860 | 0.8238 | 0.9579 | |
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| 0.0028 | 12.0 | 1272 | 0.2498 | 0.8263 | 0.8946 | 0.8591 | 0.9648 | |
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| 0.0035 | 13.0 | 1378 | 0.2407 | 0.8179 | 0.8832 | 0.8493 | 0.9643 | |
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| 0.003 | 14.0 | 1484 | 0.2475 | 0.7919 | 0.8889 | 0.8376 | 0.9631 | |
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| 0.0016 | 15.0 | 1590 | 0.2552 | 0.7975 | 0.8974 | 0.8445 | 0.9641 | |
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| 0.0016 | 16.0 | 1696 | 0.2463 | 0.8268 | 0.8974 | 0.8607 | 0.9665 | |
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| 0.0012 | 17.0 | 1802 | 0.2500 | 0.8324 | 0.8917 | 0.8611 | 0.9665 | |
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| 0.0009 | 18.0 | 1908 | 0.2629 | 0.8208 | 0.9003 | 0.8587 | 0.9653 | |
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| 0.0014 | 19.0 | 2014 | 0.2619 | 0.8182 | 0.8974 | 0.8560 | 0.9651 | |
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| 0.0006 | 20.0 | 2120 | 0.2607 | 0.8198 | 0.8946 | 0.8556 | 0.9651 | |
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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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