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abte-bert

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metadata
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: abte-bert
    results: []

abte-bert

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

  • Loss: 0.4438
  • Accuracy: 0.9133
  • F1: 0.9133

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:

  • learning_rate: 1e-05
  • train_batch_size: 128
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.5077 1.0 24 0.3039 0.9166 0.9166
0.2475 2.0 48 0.2232 0.9173 0.9173
0.2026 3.0 72 0.1930 0.9208 0.9208
0.1792 4.0 96 0.1744 0.9234 0.9234
0.1646 5.0 120 0.1671 0.9231 0.9231
0.155 6.0 144 0.1628 0.9229 0.9229
0.1483 7.0 168 0.1604 0.9266 0.9266
0.1426 8.0 192 0.1588 0.9266 0.9266
0.1383 9.0 216 0.1607 0.9264 0.9264
0.1347 10.0 240 0.1649 0.9241 0.9241
0.1317 11.0 264 0.1651 0.9245 0.9245
0.1277 12.0 288 0.1684 0.9242 0.9242
0.1256 13.0 312 0.1735 0.9250 0.9250
0.1239 14.0 336 0.1794 0.9280 0.9280
0.1223 15.0 360 0.1837 0.9235 0.9235
0.1208 16.0 384 0.1848 0.9228 0.9228
0.119 17.0 408 0.1842 0.9265 0.9265
0.1184 18.0 432 0.1895 0.9244 0.9244
0.1174 19.0 456 0.1996 0.9201 0.9201
0.117 20.0 480 0.1946 0.9220 0.9220
0.1154 21.0 504 0.2086 0.9211 0.9211
0.1141 22.0 528 0.2132 0.9212 0.9212
0.1135 23.0 552 0.2282 0.9228 0.9228
0.1123 24.0 576 0.2226 0.9214 0.9214
0.1121 25.0 600 0.2274 0.9225 0.9225
0.1109 26.0 624 0.2251 0.9224 0.9224
0.111 27.0 648 0.2419 0.9186 0.9186
0.1113 28.0 672 0.2555 0.9200 0.9200
0.1104 29.0 696 0.2439 0.9206 0.9206
0.1097 30.0 720 0.2613 0.9187 0.9187
0.1092 31.0 744 0.2519 0.9195 0.9195
0.1096 32.0 768 0.2539 0.9208 0.9208
0.1092 33.0 792 0.2647 0.9231 0.9231
0.1082 34.0 816 0.2677 0.9220 0.9220
0.1082 35.0 840 0.2693 0.9222 0.9222
0.1087 36.0 864 0.2818 0.9201 0.9201
0.1082 37.0 888 0.2773 0.9206 0.9206
0.1076 38.0 912 0.2882 0.9187 0.9187
0.1067 39.0 936 0.2776 0.9199 0.9199
0.1062 40.0 960 0.2850 0.9217 0.9217
0.1065 41.0 984 0.3098 0.9188 0.9188
0.1061 42.0 1008 0.3019 0.9191 0.9191
0.1065 43.0 1032 0.2936 0.9175 0.9175
0.1065 44.0 1056 0.3130 0.9197 0.9197
0.1056 45.0 1080 0.3119 0.9170 0.9170
0.1056 46.0 1104 0.3273 0.9171 0.9171
0.1057 47.0 1128 0.3195 0.9200 0.9200
0.1056 48.0 1152 0.3272 0.9171 0.9171
0.1046 49.0 1176 0.3276 0.9187 0.9187
0.1049 50.0 1200 0.3476 0.9152 0.9152
0.1043 51.0 1224 0.3510 0.9171 0.9171
0.1045 52.0 1248 0.3377 0.9177 0.9177
0.1046 53.0 1272 0.3232 0.9200 0.9200
0.1045 54.0 1296 0.3487 0.9147 0.9147
0.104 55.0 1320 0.3422 0.9183 0.9183
0.1041 56.0 1344 0.3609 0.9182 0.9182
0.1036 57.0 1368 0.3602 0.9172 0.9172
0.1041 58.0 1392 0.3627 0.9163 0.9163
0.1038 59.0 1416 0.3672 0.9132 0.9132
0.1044 60.0 1440 0.3597 0.9163 0.9163
0.103 61.0 1464 0.3795 0.9163 0.9163
0.104 62.0 1488 0.3635 0.9169 0.9169
0.1034 63.0 1512 0.3777 0.9146 0.9146
0.1033 64.0 1536 0.3772 0.9161 0.9161
0.1037 65.0 1560 0.3925 0.9140 0.9140
0.103 66.0 1584 0.3923 0.9157 0.9157
0.1027 67.0 1608 0.3711 0.9178 0.9178
0.103 68.0 1632 0.4019 0.9156 0.9156
0.1032 69.0 1656 0.3967 0.9134 0.9134
0.1026 70.0 1680 0.4072 0.9141 0.9141
0.1029 71.0 1704 0.4065 0.9136 0.9136
0.1023 72.0 1728 0.3933 0.9171 0.9171
0.1024 73.0 1752 0.4131 0.9109 0.9109
0.1029 74.0 1776 0.4001 0.9150 0.9150
0.1018 75.0 1800 0.4171 0.9132 0.9132
0.1022 76.0 1824 0.4151 0.9144 0.9144
0.1025 77.0 1848 0.4194 0.9149 0.9149
0.1022 78.0 1872 0.4238 0.9132 0.9132
0.1021 79.0 1896 0.4328 0.9133 0.9133
0.102 80.0 1920 0.4241 0.9113 0.9113
0.1023 81.0 1944 0.4214 0.9146 0.9146
0.1023 82.0 1968 0.4324 0.9136 0.9136
0.1021 83.0 1992 0.4251 0.9153 0.9153
0.1017 84.0 2016 0.4366 0.9138 0.9138
0.1017 85.0 2040 0.4405 0.9135 0.9135
0.1021 86.0 2064 0.4337 0.9156 0.9156
0.1019 87.0 2088 0.4343 0.9130 0.9130
0.1021 88.0 2112 0.4360 0.9145 0.9145
0.1018 89.0 2136 0.4425 0.9143 0.9143
0.1014 90.0 2160 0.4438 0.9131 0.9131
0.1017 91.0 2184 0.4409 0.9128 0.9128
0.1018 92.0 2208 0.4402 0.9136 0.9136
0.1015 93.0 2232 0.4432 0.9131 0.9131
0.1016 94.0 2256 0.4453 0.9126 0.9126
0.1017 95.0 2280 0.4495 0.9139 0.9139
0.1016 96.0 2304 0.4465 0.9135 0.9135
0.1019 97.0 2328 0.4433 0.9134 0.9134
0.1016 98.0 2352 0.4439 0.9128 0.9128
0.102 99.0 2376 0.4432 0.9134 0.9134
0.1014 100.0 2400 0.4438 0.9133 0.9133

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.5.1
  • Tokenizers 0.21.1