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Labira
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LabiraPJOK_1_500

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Transformers
TensorFlow
bert
generated_from_keras_callback
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LabiraPJOK_1_500 / README.md
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Labira
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metadata
library_name: transformers
license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_keras_callback
model-index:
  - name: Labira/LabiraPJOK_1_500
    results: []

Labira/LabiraPJOK_1_500

This model is a fine-tuned version of indolem/indobert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.0002
  • Validation Loss: 8.9202
  • Epoch: 246

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Validation Loss Epoch
0.0054 8.3302 0
0.0108 7.8442 1
0.0114 7.0958 2
0.0284 6.6490 3
0.0179 7.3034 4
0.0044 8.1785 5
0.0070 8.4039 6
0.0038 8.2728 7
0.0028 8.1154 8
0.0140 8.1207 9
0.0160 8.1384 10
0.0029 8.2978 11
0.0112 8.6940 12
0.0100 8.7433 13
0.0062 8.6486 14
0.0059 8.4821 15
0.0055 8.4559 16
0.0039 8.5136 17
0.0044 8.2783 18
0.0016 8.0974 19
0.0094 7.9739 20
0.0020 8.2513 21
0.0008 8.4637 22
0.0039 8.2813 23
0.0017 8.2027 24
0.0018 8.2722 25
0.0015 8.3875 26
0.0013 8.4975 27
0.0013 8.6171 28
0.0009 8.7272 29
0.0010 8.8335 30
0.0007 8.9168 31
0.0007 8.9992 32
0.0006 9.0661 33
0.0007 9.1103 34
0.0004 9.1424 35
0.0008 9.1573 36
0.0006 9.1666 37
0.0008 9.1732 38
0.0004 9.1781 39
0.0006 9.1867 40
0.0005 9.1986 41
0.0005 9.2203 42
0.0005 9.2512 43
0.0006 9.2889 44
0.0005 9.3360 45
0.0007 9.3759 46
0.0004 9.4144 47
0.0006 9.4461 48
0.0004 9.4718 49
0.0005 9.5113 50
0.0004 9.5425 51
0.0003 9.5667 52
0.0015 9.5468 53
0.0003 9.4515 54
0.0005 9.3881 55
0.0006 9.3797 56
0.0006 9.3887 57
0.0003 9.4038 58
0.0004 9.4206 59
0.0003 9.4417 60
0.0003 9.4627 61
0.0003 9.4775 62
0.0004 9.4930 63
0.0009 9.5593 64
0.0003 9.6068 65
0.0003 9.6416 66
0.0003 9.6715 67
0.0003 9.6956 68
0.0004 9.7146 69
0.0010 9.7344 70
0.0002 9.7946 71
0.0003 9.7965 72
0.0034 9.7113 73
0.0004 9.5730 74
0.0005 9.4858 75
0.0009 9.5826 76
0.0006 9.6923 77
0.0005 9.8243 78
0.0005 9.9368 79
0.0007 10.0514 80
0.0006 10.1386 81
0.0010 10.1427 82
0.0005 9.9261 83
0.0011 9.8122 84
0.0003 9.8724 85
0.0081 9.5494 86
0.0151 8.3043 87
0.0425 9.1449 88
0.0076 8.8560 89
0.0113 8.2403 90
0.0446 7.5457 91
0.0264 7.4204 92
0.1545 8.0820 93
0.3878 8.2238 94
0.4155 6.1718 95
0.0410 5.0625 96
0.0768 4.8214 97
0.0514 4.8477 98
0.0150 5.2002 99
0.0328 5.6224 100
0.0260 5.9887 101
0.0040 6.2793 102
0.0076 6.3696 103
0.0013 6.3642 104
0.0075 6.4379 105
0.0015 6.6379 106
0.0010 6.7736 107
0.0023 6.8582 108
0.0056 6.8884 109
0.0011 6.9125 110
0.0014 6.9437 111
0.0014 6.9807 112
0.0010 7.0239 113
0.0006 7.0602 114
0.0006 7.0919 115
0.0005 7.1213 116
0.0008 7.1457 117
0.0006 7.1679 118
0.0009 7.1871 119
0.0288 7.3166 120
0.0007 7.1397 121
0.0033 6.9025 122
0.0020 6.8509 123
0.0068 6.9533 124
0.0066 7.2446 125
0.0035 7.5351 126
0.0019 7.7354 127
0.0021 7.8376 128
0.0007 7.9071 129
0.0012 7.9566 130
0.0009 8.0014 131
0.0013 8.0186 132
0.0015 8.0123 133
0.0009 7.9870 134
0.0008 7.9685 135
0.0005 7.9599 136
0.0005 7.9553 137
0.0005 7.9574 138
0.0005 7.9631 139
0.0010 7.9780 140
0.0006 7.9910 141
0.0006 8.0078 142
0.0004 8.0283 143
0.0006 8.0500 144
0.0005 8.0704 145
0.0008 8.0899 146
0.0003 8.1078 147
0.0003 8.1243 148
0.0005 8.1384 149
0.0005 8.1534 150
0.0003 8.1678 151
0.0003 8.1827 152
0.0002 8.1955 153
0.0004 8.2093 154
0.0003 8.2218 155
0.0003 8.2338 156
0.0003 8.2454 157
0.0003 8.2566 158
0.0004 8.2696 159
0.0006 8.2696 160
0.0003 8.2700 161
0.0003 8.2745 162
0.0004 8.2834 163
0.0004 8.2918 164
0.0003 8.3035 165
0.0004 8.3182 166
0.0005 8.3357 167
0.0003 8.3499 168
0.0002 8.3616 169
0.0005 8.3759 170
0.0003 8.3901 171
0.0002 8.4020 172
0.0004 8.4105 173
0.0004 8.4120 174
0.0005 8.4166 175
0.0003 8.4209 176
0.0003 8.4287 177
0.0011 8.4219 178
0.0005 8.3854 179
0.0003 8.3589 180
0.0003 8.3630 181
0.0002 8.3680 182
0.0003 8.3735 183
0.0003 8.3812 184
0.0003 8.3882 185
0.0003 8.3937 186
0.0002 8.3989 187
0.0003 8.4022 188
0.0003 8.4048 189
0.0003 8.4102 190
0.0004 8.4197 191
0.0003 8.4328 192
0.0004 8.4468 193
0.0002 8.4609 194
0.0011 8.4712 195
0.0003 8.4735 196
0.0002 8.4789 197
0.0007 8.4928 198
0.0002 8.5066 199
0.0003 8.5205 200
0.0003 8.5368 201
0.0003 8.5531 202
0.0002 8.5676 203
0.0002 8.5815 204
0.0003 8.5989 205
0.0003 8.6161 206
0.0001 8.6305 207
0.0003 8.6473 208
0.0003 8.6626 209
0.0003 8.6764 210
0.0002 8.6899 211
0.0002 8.7019 212
0.0002 8.7119 213
0.0002 8.7212 214
0.0002 8.7302 215
0.0004 8.7417 216
0.0002 8.7514 217
0.0002 8.7593 218
0.0003 8.7690 219
0.0002 8.7771 220
0.0002 8.7845 221
0.0001 8.7917 222
0.0002 8.7980 223
0.0002 8.8040 224
0.0003 8.8093 225
0.0003 8.8150 226
0.0002 8.8209 227
0.0003 8.8271 228
0.0002 8.8329 229
0.0002 8.8378 230
0.0002 8.8429 231
0.0003 8.8493 232
0.0003 8.8575 233
0.0004 8.8548 234
0.0003 8.8510 235
0.0001 8.8501 236
0.0003 8.8473 237
0.0003 8.8561 238
0.0003 8.8667 239
0.0002 8.8767 240
0.0001 8.8847 241
0.0002 8.8915 242
0.0002 8.8980 243
0.0002 8.9038 244
0.0002 8.9119 245
0.0002 8.9202 246

Framework versions

  • Transformers 4.44.2
  • TensorFlow 2.17.0
  • Datasets 3.0.1
  • Tokenizers 0.19.1