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--- |
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license: mit |
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base_model: cahya/distilbert-base-indonesian |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: distilbert-base-indonesian-finetuned-PRDECT-ID |
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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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# distilbert-base-indonesian-finetuned-PRDECT-ID |
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This model is a fine-tuned version of [cahya/distilbert-base-indonesian](https://huggingface.co/cahya/distilbert-base-indonesian) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0002 |
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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: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.9507 | 1.0 | 41 | 0.8377 | |
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| 0.0765 | 2.0 | 82 | 0.0212 | |
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| 0.0025 | 3.0 | 123 | 0.0020 | |
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| 0.0013 | 4.0 | 164 | 0.0013 | |
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| 0.0009 | 5.0 | 205 | 0.0009 | |
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| 0.0007 | 6.0 | 246 | 0.0007 | |
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| 0.0005 | 7.0 | 287 | 0.0006 | |
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| 0.0004 | 8.0 | 328 | 0.0005 | |
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| 0.0003 | 9.0 | 369 | 0.0004 | |
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| 0.0002 | 10.0 | 410 | 0.0003 | |
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| 0.0002 | 11.0 | 451 | 0.0003 | |
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| 0.0002 | 12.0 | 492 | 0.0003 | |
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| 0.0002 | 13.0 | 533 | 0.0002 | |
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| 0.0001 | 14.0 | 574 | 0.0002 | |
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| 0.0001 | 15.0 | 615 | 0.0002 | |
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| 0.0001 | 16.0 | 656 | 0.0002 | |
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| 0.0001 | 17.0 | 697 | 0.0002 | |
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| 0.0001 | 18.0 | 738 | 0.0002 | |
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| 0.0001 | 19.0 | 779 | 0.0002 | |
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| 0.0001 | 20.0 | 820 | 0.0002 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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