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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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- 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-lora-r8-4 |
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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-lora-r8-4 |
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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: 0.1784 |
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- Precision: 0.6597 |
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- Recall: 0.8120 |
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- F1: 0.7280 |
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- Accuracy: 0.9434 |
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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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| 1.2537 | 1.0 | 106 | 0.7370 | 0.0 | 0.0 | 0.0 | 0.8366 | |
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| 0.7093 | 2.0 | 212 | 0.6298 | 0.1667 | 0.0028 | 0.0056 | 0.8373 | |
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| 0.6232 | 3.0 | 318 | 0.5443 | 0.2727 | 0.0171 | 0.0322 | 0.8417 | |
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| 0.5363 | 4.0 | 424 | 0.4559 | 0.3301 | 0.0969 | 0.1498 | 0.8624 | |
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| 0.4591 | 5.0 | 530 | 0.3863 | 0.4744 | 0.2906 | 0.3604 | 0.8929 | |
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| 0.387 | 6.0 | 636 | 0.3272 | 0.5789 | 0.5641 | 0.5714 | 0.9190 | |
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| 0.3252 | 7.0 | 742 | 0.2811 | 0.6035 | 0.6895 | 0.6436 | 0.9291 | |
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| 0.2874 | 8.0 | 848 | 0.2455 | 0.6108 | 0.7066 | 0.6552 | 0.9313 | |
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| 0.2588 | 9.0 | 954 | 0.2285 | 0.6179 | 0.7464 | 0.6761 | 0.9333 | |
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| 0.2393 | 10.0 | 1060 | 0.2153 | 0.6362 | 0.7721 | 0.6976 | 0.9363 | |
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| 0.224 | 11.0 | 1166 | 0.2062 | 0.6339 | 0.7892 | 0.7030 | 0.9387 | |
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| 0.2137 | 12.0 | 1272 | 0.2002 | 0.6473 | 0.7949 | 0.7136 | 0.9387 | |
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| 0.2052 | 13.0 | 1378 | 0.1889 | 0.6611 | 0.7949 | 0.7219 | 0.9424 | |
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| 0.2039 | 14.0 | 1484 | 0.1862 | 0.6690 | 0.8063 | 0.7313 | 0.9431 | |
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| 0.1975 | 15.0 | 1590 | 0.1868 | 0.6682 | 0.8091 | 0.7320 | 0.9431 | |
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| 0.1936 | 16.0 | 1696 | 0.1837 | 0.6628 | 0.8177 | 0.7321 | 0.9427 | |
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| 0.1908 | 17.0 | 1802 | 0.1825 | 0.6620 | 0.8148 | 0.7305 | 0.9427 | |
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| 0.1885 | 18.0 | 1908 | 0.1806 | 0.6582 | 0.8120 | 0.7270 | 0.9431 | |
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| 0.1877 | 19.0 | 2014 | 0.1783 | 0.6581 | 0.8063 | 0.7247 | 0.9431 | |
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| 0.1858 | 20.0 | 2120 | 0.1784 | 0.6597 | 0.8120 | 0.7280 | 0.9434 | |
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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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