--- 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](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0002 - Validation Loss: 9.1787 - Epoch: 309 ## 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 | | 0.0002 | 8.9279 | 247 | | 0.0002 | 8.9345 | 248 | | 0.0001 | 8.9400 | 249 | | 0.0001 | 8.9441 | 250 | | 0.0002 | 8.9473 | 251 | | 0.0001 | 8.9504 | 252 | | 0.0002 | 8.9537 | 253 | | 0.0001 | 8.9574 | 254 | | 0.0002 | 8.9610 | 255 | | 0.0002 | 8.9650 | 256 | | 0.0002 | 8.9691 | 257 | | 0.0001 | 8.9724 | 258 | | 0.0002 | 8.9753 | 259 | | 0.0002 | 8.9769 | 260 | | 0.0001 | 8.9793 | 261 | | 0.0002 | 8.9827 | 262 | | 0.0003 | 8.9841 | 263 | | 0.0001 | 8.9854 | 264 | | 0.0001 | 8.9873 | 265 | | 0.0010 | 8.9858 | 266 | | 0.0006 | 8.9368 | 267 | | 0.0002 | 8.9002 | 268 | | 0.0003 | 8.8744 | 269 | | 0.0002 | 8.8568 | 270 | | 0.0001 | 8.8456 | 271 | | 0.0002 | 8.8382 | 272 | | 0.0001 | 8.8340 | 273 | | 0.0001 | 8.8317 | 274 | | 0.0002 | 8.8310 | 275 | | 0.0001 | 8.8316 | 276 | | 0.0002 | 8.8335 | 277 | | 0.0002 | 8.8372 | 278 | | 0.0003 | 8.8456 | 279 | | 0.0008 | 8.9045 | 280 | | 0.0002 | 8.9467 | 281 | | 0.0002 | 8.9773 | 282 | | 0.0002 | 9.0012 | 283 | | 0.0001 | 9.0195 | 284 | | 0.0002 | 9.0337 | 285 | | 0.0002 | 9.0464 | 286 | | 0.0001 | 9.0564 | 287 | | 0.0003 | 9.0657 | 288 | | 0.0001 | 9.0737 | 289 | | 0.0002 | 9.0798 | 290 | | 0.0001 | 9.0844 | 291 | | 0.0002 | 9.0884 | 292 | | 0.0002 | 9.0919 | 293 | | 0.0006 | 9.0998 | 294 | | 0.0001 | 9.1140 | 295 | | 0.0002 | 9.1256 | 296 | | 0.0001 | 9.1349 | 297 | | 0.0001 | 9.1424 | 298 | | 0.0001 | 9.1486 | 299 | | 0.0001 | 9.1534 | 300 | | 0.0001 | 9.1576 | 301 | | 0.0001 | 9.1614 | 302 | | 0.0001 | 9.1649 | 303 | | 0.0001 | 9.1676 | 304 | | 0.0001 | 9.1701 | 305 | | 0.0001 | 9.1720 | 306 | | 0.0002 | 9.1744 | 307 | | 0.0002 | 9.1769 | 308 | | 0.0002 | 9.1787 | 309 | ### Framework versions - Transformers 4.44.2 - TensorFlow 2.17.0 - Datasets 3.0.1 - Tokenizers 0.19.1