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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-base-0 |
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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-0 |
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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: 0.2749 |
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- Precision: 0.8234 |
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- Recall: 0.8964 |
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- F1: 0.8584 |
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- Accuracy: 0.9631 |
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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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| 0.3551 | 1.0 | 106 | 0.1873 | 0.6789 | 0.8757 | 0.7649 | 0.9414 | |
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| 0.1199 | 2.0 | 212 | 0.1308 | 0.7602 | 0.8817 | 0.8164 | 0.9611 | |
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| 0.0746 | 3.0 | 318 | 0.1383 | 0.7755 | 0.8787 | 0.8239 | 0.9618 | |
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| 0.0497 | 4.0 | 424 | 0.1717 | 0.7922 | 0.8462 | 0.8183 | 0.9554 | |
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| 0.0289 | 5.0 | 530 | 0.1706 | 0.8027 | 0.8787 | 0.8390 | 0.9621 | |
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| 0.023 | 6.0 | 636 | 0.1929 | 0.7688 | 0.8757 | 0.8188 | 0.9585 | |
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| 0.0161 | 7.0 | 742 | 0.2457 | 0.7769 | 0.8757 | 0.8234 | 0.9539 | |
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| 0.0106 | 8.0 | 848 | 0.2450 | 0.7926 | 0.8817 | 0.8347 | 0.9572 | |
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| 0.0065 | 9.0 | 954 | 0.2315 | 0.8150 | 0.8994 | 0.8551 | 0.9629 | |
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| 0.0053 | 10.0 | 1060 | 0.2373 | 0.8147 | 0.8846 | 0.8482 | 0.9626 | |
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| 0.004 | 11.0 | 1166 | 0.2421 | 0.8283 | 0.8846 | 0.8555 | 0.9639 | |
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| 0.003 | 12.0 | 1272 | 0.2572 | 0.808 | 0.8964 | 0.8499 | 0.9621 | |
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| 0.0027 | 13.0 | 1378 | 0.2516 | 0.8135 | 0.8905 | 0.8503 | 0.9616 | |
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| 0.0012 | 14.0 | 1484 | 0.2636 | 0.8123 | 0.8964 | 0.8523 | 0.9649 | |
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| 0.002 | 15.0 | 1590 | 0.2672 | 0.8091 | 0.8905 | 0.8479 | 0.9626 | |
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| 0.0012 | 16.0 | 1696 | 0.2610 | 0.8130 | 0.8876 | 0.8487 | 0.9634 | |
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| 0.001 | 17.0 | 1802 | 0.2694 | 0.8251 | 0.8935 | 0.8580 | 0.9631 | |
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| 0.0012 | 18.0 | 1908 | 0.2815 | 0.8177 | 0.9024 | 0.8579 | 0.9626 | |
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| 0.0012 | 19.0 | 2014 | 0.2723 | 0.8229 | 0.8935 | 0.8567 | 0.9629 | |
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| 0.0008 | 20.0 | 2120 | 0.2749 | 0.8234 | 0.8964 | 0.8584 | 0.9631 | |
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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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