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--- |
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license: mit |
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base_model: indobenchmark/indobert-base-p2 |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: koneksi_model |
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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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# koneksi_model |
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This model is a fine-tuned version of [indobenchmark/indobert-base-p2](https://huggingface.co/indobenchmark/indobert-base-p2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4885 |
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- Accuracy: 0.8177 |
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- F1: 0.8087 |
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- Precision: 0.8916 |
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- Recall: 0.74 |
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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: 2e-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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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 96 | 0.4936 | 0.7760 | 0.7817 | 0.7938 | 0.77 | |
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| No log | 2.0 | 192 | 0.4885 | 0.8177 | 0.8087 | 0.8916 | 0.74 | |
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| No log | 3.0 | 288 | 0.6119 | 0.7552 | 0.7662 | 0.7624 | 0.77 | |
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| No log | 4.0 | 384 | 1.0256 | 0.7552 | 0.7314 | 0.8533 | 0.64 | |
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| No log | 5.0 | 480 | 1.2790 | 0.7604 | 0.7629 | 0.7872 | 0.74 | |
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| 0.2515 | 6.0 | 576 | 1.3453 | 0.7656 | 0.7716 | 0.7835 | 0.76 | |
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| 0.2515 | 7.0 | 672 | 1.4966 | 0.7708 | 0.7864 | 0.7642 | 0.81 | |
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| 0.2515 | 8.0 | 768 | 1.4197 | 0.7708 | 0.7660 | 0.8182 | 0.72 | |
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| 0.2515 | 9.0 | 864 | 1.5297 | 0.7760 | 0.7861 | 0.7822 | 0.79 | |
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| 0.2515 | 10.0 | 960 | 1.5265 | 0.7708 | 0.78 | 0.78 | 0.78 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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