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Dhanang
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performa_model

Text Classification
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  1. README.md +29 -16
  2. runs/Jan14_09-27-03_fcd99226172a/events.out.tfevents.1705224425.fcd99226172a.370.2 +2 -2
  3. runs/Jan14_09-27-03_fcd99226172a/events.out.tfevents.1705224863.fcd99226172a.370.3 +3 -0
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.4147
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- - Accuracy: 0.8230
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- - F1: 0.7904
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- - Precision: 0.8488
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- - Recall: 0.7395
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  ## Model description
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@@ -49,22 +49,35 @@ The following hyperparameters were used during training:
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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 | 370 | 0.4182 | 0.8297 | 0.8108 | 0.8133 | 0.8084 |
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- | 0.4155 | 2.0 | 740 | 0.4147 | 0.8230 | 0.7904 | 0.8488 | 0.7395 |
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- | 0.304 | 3.0 | 1110 | 0.4912 | 0.8162 | 0.7952 | 0.8 | 0.7904 |
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- | 0.304 | 4.0 | 1480 | 0.8223 | 0.8014 | 0.7879 | 0.7604 | 0.8174 |
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- | 0.1698 | 5.0 | 1850 | 0.8766 | 0.8108 | 0.7935 | 0.7820 | 0.8054 |
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- | 0.0868 | 6.0 | 2220 | 1.2547 | 0.7919 | 0.7775 | 0.7514 | 0.8054 |
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- | 0.0357 | 7.0 | 2590 | 1.2560 | 0.7946 | 0.7847 | 0.7446 | 0.8293 |
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- | 0.0357 | 8.0 | 2960 | 1.3313 | 0.8095 | 0.7886 | 0.7898 | 0.7874 |
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- | 0.0159 | 9.0 | 3330 | 1.3923 | 0.8122 | 0.7965 | 0.7794 | 0.8144 |
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- | 0.0066 | 10.0 | 3700 | 1.4022 | 0.8149 | 0.8006 | 0.7790 | 0.8234 |
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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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: 0.5465
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+ - Accuracy: 0.8122
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+ - F1: 0.8102
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+ - Precision: 0.8105
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+ - Recall: 0.8100
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  ## Model description
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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: 5
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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 | 0.14 | 50 | 0.4595 | 0.7946 | 0.7926 | 0.7926 | 0.7926 |
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+ | No log | 0.27 | 100 | 0.4523 | 0.7946 | 0.7946 | 0.7995 | 0.8009 |
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+ | No log | 0.41 | 150 | 0.4501 | 0.8122 | 0.8098 | 0.8110 | 0.8089 |
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+ | No log | 0.54 | 200 | 0.4676 | 0.7811 | 0.7709 | 0.7965 | 0.7678 |
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+ | No log | 0.68 | 250 | 0.4551 | 0.8135 | 0.8099 | 0.8149 | 0.8077 |
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+ | No log | 0.81 | 300 | 0.4422 | 0.8162 | 0.8152 | 0.8146 | 0.8168 |
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+ | No log | 0.95 | 350 | 0.4336 | 0.8162 | 0.8137 | 0.8154 | 0.8126 |
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+ | No log | 1.08 | 400 | 0.4645 | 0.8189 | 0.8164 | 0.8182 | 0.8153 |
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+ | No log | 1.22 | 450 | 0.4805 | 0.8243 | 0.8236 | 0.8231 | 0.8258 |
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+ | 0.4139 | 1.35 | 500 | 0.4984 | 0.8068 | 0.8053 | 0.8048 | 0.8061 |
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+ | 0.4139 | 1.49 | 550 | 0.4506 | 0.8149 | 0.8137 | 0.8131 | 0.8148 |
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+ | 0.4139 | 1.62 | 600 | 0.4364 | 0.8216 | 0.8201 | 0.8198 | 0.8204 |
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+ | 0.4139 | 1.76 | 650 | 0.4889 | 0.7892 | 0.7892 | 0.7992 | 0.7978 |
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+ | 0.4139 | 1.89 | 700 | 0.4348 | 0.8108 | 0.8105 | 0.8114 | 0.8143 |
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+ | 0.4139 | 2.03 | 750 | 0.4537 | 0.8068 | 0.8056 | 0.8050 | 0.8069 |
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+ | 0.4139 | 2.16 | 800 | 0.5296 | 0.7905 | 0.7905 | 0.7947 | 0.7964 |
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+ | 0.4139 | 2.3 | 850 | 0.5819 | 0.7946 | 0.7943 | 0.7955 | 0.7982 |
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+ | 0.4139 | 2.43 | 900 | 0.5868 | 0.8122 | 0.8110 | 0.8104 | 0.8124 |
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+ | 0.4139 | 2.57 | 950 | 0.5613 | 0.8081 | 0.8050 | 0.8081 | 0.8034 |
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+ | 0.2978 | 2.7 | 1000 | 0.5465 | 0.8122 | 0.8102 | 0.8105 | 0.8100 |
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+ | 0.2978 | 2.84 | 1050 | 0.5665 | 0.8041 | 0.8022 | 0.8022 | 0.8023 |
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+ | 0.2978 | 2.97 | 1100 | 0.5876 | 0.7932 | 0.7924 | 0.7921 | 0.7946 |
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+ | 0.2978 | 3.11 | 1150 | 0.7388 | 0.8014 | 0.8000 | 0.7994 | 0.8009 |
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  ### Framework versions
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