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nerui-lora-r16-1

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metadata
language:
  - id
license: mit
base_model: indolem/indobert-base-uncased
tags:
  - generated_from_trainer
model-index:
  - name: nerui-lora-r16-1
    results: []

nerui-lora-r16-1

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:

  • Loss: 0.0342
  • Location Precision: 0.9316
  • Location Recall: 0.9397
  • Location F1: 0.9356
  • Location Number: 116
  • Organization Precision: 0.9484
  • Organization Recall: 0.9304
  • Organization F1: 0.9393
  • Organization Number: 158
  • Person Precision: 0.984
  • Person Recall: 0.9919
  • Person F1: 0.9880
  • Person Number: 124
  • Overall Precision: 0.9547
  • Overall Recall: 0.9523
  • Overall F1: 0.9535
  • Overall Accuracy: 0.9896

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: 5e-05
  • train_batch_size: 16
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 100.0

Training results

Training Loss Epoch Step Validation Loss Location Precision Location Recall Location F1 Location Number Organization Precision Organization Recall Organization F1 Organization Number Person Precision Person Recall Person F1 Person Number Overall Precision Overall Recall Overall F1 Overall Accuracy
1.0545 1.0 96 0.6622 0.0 0.0 0.0 116 0.0 0.0 0.0 158 0.0 0.0 0.0 124 0.0 0.0 0.0 0.8394
0.64 2.0 192 0.5206 0.0 0.0 0.0 116 0.5 0.0127 0.0247 158 0.0 0.0 0.0 124 0.3333 0.0050 0.0099 0.8400
0.503 3.0 288 0.3728 0.0833 0.0086 0.0156 116 0.3625 0.1835 0.2437 158 0.36 0.2903 0.3214 124 0.3438 0.1658 0.2237 0.8718
0.3537 4.0 384 0.2518 0.3947 0.2586 0.3125 116 0.4885 0.5380 0.5120 158 0.5521 0.7258 0.6272 124 0.4964 0.5151 0.5055 0.9198
0.2513 5.0 480 0.1812 0.6111 0.5690 0.5893 116 0.5979 0.7342 0.6591 158 0.8028 0.9194 0.8571 124 0.6667 0.7437 0.7031 0.9498
0.1948 6.0 576 0.1359 0.7438 0.7759 0.7595 116 0.7368 0.7975 0.7660 158 0.8905 0.9839 0.9349 124 0.7879 0.8492 0.8174 0.9657
0.1623 7.0 672 0.1109 0.7917 0.8190 0.8051 116 0.7619 0.8101 0.7853 158 0.9104 0.9839 0.9457 124 0.8175 0.8668 0.8415 0.9701
0.1397 8.0 768 0.0954 0.8083 0.8362 0.8220 116 0.7976 0.8481 0.8221 158 0.9389 0.9919 0.9647 124 0.8449 0.8894 0.8666 0.9739
0.1266 9.0 864 0.0877 0.8189 0.8966 0.8560 116 0.8155 0.8671 0.8405 158 0.9318 0.9919 0.9609 124 0.8525 0.9146 0.8824 0.9761
0.1157 10.0 960 0.0731 0.8607 0.9052 0.8824 116 0.8519 0.8734 0.8625 158 0.9609 0.9919 0.9762 124 0.8883 0.9196 0.9037 0.9800
0.1111 11.0 1056 0.0673 0.8760 0.9138 0.8945 116 0.8606 0.8987 0.8793 158 0.9685 0.9919 0.9801 124 0.8983 0.9322 0.9149 0.9813
0.1044 12.0 1152 0.0635 0.8760 0.9138 0.8945 116 0.8554 0.8987 0.8765 158 0.9685 0.9919 0.9801 124 0.8961 0.9322 0.9138 0.9811
0.098 13.0 1248 0.0578 0.8898 0.9052 0.8974 116 0.8589 0.8861 0.8723 158 0.9762 0.9919 0.9840 124 0.9042 0.9246 0.9143 0.9816
0.0939 14.0 1344 0.0559 0.875 0.9052 0.8898 116 0.8642 0.8861 0.8750 158 0.9762 0.9919 0.9840 124 0.9020 0.9246 0.9132 0.9819
0.091 15.0 1440 0.0558 0.8824 0.9052 0.8936 116 0.8402 0.8987 0.8685 158 0.9685 0.9919 0.9801 124 0.8916 0.9296 0.9102 0.9816
0.088 16.0 1536 0.0555 0.875 0.9052 0.8898 116 0.8452 0.8987 0.8712 158 0.9535 0.9919 0.9723 124 0.8873 0.9296 0.9080 0.9811
0.0857 17.0 1632 0.0523 0.8824 0.9052 0.8936 116 0.8868 0.8924 0.8896 158 0.984 0.9919 0.9880 124 0.9156 0.9271 0.9213 0.9846
0.0809 18.0 1728 0.0498 0.8678 0.9052 0.8861 116 0.8659 0.8987 0.8820 158 0.984 0.9919 0.9880 124 0.9024 0.9296 0.9158 0.9833
0.0773 19.0 1824 0.0482 0.8898 0.9052 0.8974 116 0.8827 0.9051 0.8938 158 0.984 0.9919 0.9880 124 0.9160 0.9322 0.9240 0.9844
0.0765 20.0 1920 0.0521 0.8833 0.9138 0.8983 116 0.8571 0.9114 0.8834 158 0.9685 0.9919 0.9801 124 0.8988 0.9372 0.9176 0.9822
0.0754 21.0 2016 0.0484 0.875 0.9052 0.8898 116 0.8735 0.9177 0.8951 158 0.984 0.9919 0.9880 124 0.9075 0.9372 0.9221 0.9841
0.072 22.0 2112 0.0469 0.875 0.9052 0.8898 116 0.8606 0.8987 0.8793 158 0.984 0.9919 0.9880 124 0.9024 0.9296 0.9158 0.9835
0.0689 23.0 2208 0.0440 0.8898 0.9052 0.8974 116 0.8944 0.9114 0.9028 158 0.984 0.9919 0.9880 124 0.9208 0.9347 0.9277 0.9844
0.0697 24.0 2304 0.0456 0.8974 0.9052 0.9013 116 0.8968 0.8797 0.8882 158 0.984 0.9919 0.9880 124 0.9244 0.9221 0.9233 0.9846
0.0656 25.0 2400 0.0436 0.8983 0.9138 0.9060 116 0.8812 0.8924 0.8868 158 0.984 0.9919 0.9880 124 0.9181 0.9296 0.9238 0.9846
0.0658 26.0 2496 0.0427 0.8974 0.9052 0.9013 116 0.8704 0.8924 0.8812 158 0.984 0.9919 0.9880 124 0.9134 0.9271 0.9202 0.9841
0.065 27.0 2592 0.0421 0.9052 0.9052 0.9052 116 0.8834 0.9114 0.8972 158 0.984 0.9919 0.9880 124 0.9208 0.9347 0.9277 0.9855
0.0613 28.0 2688 0.0418 0.8833 0.9138 0.8983 116 0.8882 0.9051 0.8966 158 0.984 0.9919 0.9880 124 0.9163 0.9347 0.9254 0.9855
0.0591 29.0 2784 0.0398 0.9060 0.9138 0.9099 116 0.8882 0.9051 0.8966 158 0.984 0.9919 0.9880 124 0.9231 0.9347 0.9288 0.9874
0.06 30.0 2880 0.0395 0.9060 0.9138 0.9099 116 0.8994 0.9051 0.9022 158 0.984 0.9919 0.9880 124 0.9277 0.9347 0.9312 0.9865
0.0566 31.0 2976 0.0386 0.8983 0.9138 0.9060 116 0.8827 0.9051 0.8938 158 0.984 0.9919 0.9880 124 0.9185 0.9347 0.9265 0.9863
0.0566 32.0 3072 0.0392 0.8889 0.8966 0.8927 116 0.9045 0.8987 0.9016 158 0.984 0.9919 0.9880 124 0.9248 0.9271 0.9260 0.9857
0.0566 33.0 3168 0.0398 0.8992 0.9224 0.9106 116 0.9045 0.8987 0.9016 158 0.984 0.9919 0.9880 124 0.9277 0.9347 0.9312 0.9865
0.0568 34.0 3264 0.0396 0.9224 0.9224 0.9224 116 0.8951 0.9177 0.9062 158 0.984 0.9919 0.9880 124 0.9305 0.9422 0.9363 0.9871
0.0532 35.0 3360 0.0379 0.8983 0.9138 0.9060 116 0.9051 0.9051 0.9051 158 0.984 0.9919 0.9880 124 0.9277 0.9347 0.9312 0.9871
0.052 36.0 3456 0.0403 0.9231 0.9310 0.9270 116 0.9012 0.9241 0.9125 158 0.984 0.9919 0.9880 124 0.9332 0.9472 0.9401 0.9879
0.0516 37.0 3552 0.0386 0.8983 0.9138 0.9060 116 0.9 0.9114 0.9057 158 0.984 0.9919 0.9880 124 0.9256 0.9372 0.9313 0.9874
0.0497 38.0 3648 0.0378 0.8992 0.9224 0.9106 116 0.8994 0.9051 0.9022 158 0.984 0.9919 0.9880 124 0.9256 0.9372 0.9313 0.9879
0.052 39.0 3744 0.0366 0.9138 0.9138 0.9138 116 0.9006 0.9177 0.9091 158 0.984 0.9919 0.9880 124 0.9303 0.9397 0.9350 0.9885
0.0472 40.0 3840 0.0367 0.9138 0.9138 0.9138 116 0.8987 0.8987 0.8987 158 0.984 0.9919 0.9880 124 0.9298 0.9322 0.9310 0.9868
0.0486 41.0 3936 0.0388 0.9076 0.9310 0.9191 116 0.9074 0.9304 0.9187 158 0.984 0.9919 0.9880 124 0.9310 0.9497 0.9403 0.9882
0.047 42.0 4032 0.0375 0.9068 0.9224 0.9145 116 0.9161 0.8987 0.9073 158 0.984 0.9919 0.9880 124 0.9347 0.9347 0.9347 0.9874
0.0481 43.0 4128 0.0380 0.8983 0.9138 0.9060 116 0.9051 0.9051 0.9051 158 0.984 0.9919 0.9880 124 0.9277 0.9347 0.9312 0.9860
0.0468 44.0 4224 0.0391 0.9231 0.9310 0.9270 116 0.9062 0.9177 0.9119 158 0.984 0.9919 0.9880 124 0.9353 0.9447 0.94 0.9876
0.0473 45.0 4320 0.0366 0.8992 0.9224 0.9106 116 0.9045 0.8987 0.9016 158 0.984 0.9919 0.9880 124 0.9277 0.9347 0.9312 0.9868
0.0441 46.0 4416 0.0372 0.9 0.9310 0.9153 116 0.9006 0.9177 0.9091 158 0.984 0.9919 0.9880 124 0.9261 0.9447 0.9353 0.9887
0.0441 47.0 4512 0.0375 0.9224 0.9224 0.9224 116 0.9068 0.9241 0.9154 158 0.984 0.9919 0.9880 124 0.9353 0.9447 0.94 0.9887
0.0416 48.0 4608 0.0359 0.9237 0.9397 0.9316 116 0.9363 0.9304 0.9333 158 0.984 0.9919 0.9880 124 0.9475 0.9523 0.9499 0.9898
0.0446 49.0 4704 0.0355 0.9153 0.9310 0.9231 116 0.8931 0.8987 0.8959 158 0.984 0.9919 0.9880 124 0.9279 0.9372 0.9325 0.9876
0.0425 50.0 4800 0.0366 0.9160 0.9397 0.9277 116 0.9 0.9114 0.9057 158 0.984 0.9919 0.9880 124 0.9307 0.9447 0.9377 0.9887
0.0422 51.0 4896 0.0364 0.9153 0.9310 0.9231 116 0.9167 0.9051 0.9108 158 0.984 0.9919 0.9880 124 0.9373 0.9397 0.9385 0.9871
0.0409 52.0 4992 0.0357 0.9145 0.9224 0.9185 116 0.9074 0.9304 0.9187 158 0.984 0.9919 0.9880 124 0.9332 0.9472 0.9401 0.9896
0.0414 53.0 5088 0.0359 0.9231 0.9310 0.9270 116 0.9136 0.9367 0.9250 158 0.984 0.9919 0.9880 124 0.9381 0.9523 0.9451 0.9901
0.0403 54.0 5184 0.0353 0.9231 0.9310 0.9270 116 0.8963 0.9304 0.9130 158 0.984 0.9919 0.9880 124 0.9310 0.9497 0.9403 0.9896
0.0393 55.0 5280 0.0352 0.9145 0.9224 0.9185 116 0.9136 0.9367 0.9250 158 0.984 0.9919 0.9880 124 0.9356 0.9497 0.9426 0.9898
0.0405 56.0 5376 0.0359 0.9237 0.9397 0.9316 116 0.9430 0.9430 0.9430 158 0.984 0.9919 0.9880 124 0.9501 0.9573 0.9537 0.9901
0.0404 57.0 5472 0.0370 0.9160 0.9397 0.9277 116 0.9371 0.9430 0.9401 158 0.984 0.9919 0.9880 124 0.9454 0.9573 0.9513 0.9896
0.0398 58.0 5568 0.0355 0.9316 0.9397 0.9356 116 0.9308 0.9367 0.9338 158 0.984 0.9919 0.9880 124 0.9476 0.9548 0.9512 0.9904
0.0382 59.0 5664 0.0355 0.9397 0.9397 0.9397 116 0.9551 0.9430 0.9490 158 0.984 0.9919 0.9880 124 0.9597 0.9573 0.9585 0.9904
0.0396 60.0 5760 0.0344 0.9160 0.9397 0.9277 116 0.9125 0.9241 0.9182 158 0.984 0.9919 0.9880 124 0.9356 0.9497 0.9426 0.9893
0.0362 61.0 5856 0.0356 0.9231 0.9310 0.9270 116 0.9226 0.9051 0.9137 158 0.984 0.9919 0.9880 124 0.9421 0.9397 0.9409 0.9879
0.037 62.0 5952 0.0360 0.9237 0.9397 0.9316 116 0.9167 0.9051 0.9108 158 0.984 0.9919 0.9880 124 0.9398 0.9422 0.9410 0.9882
0.0386 63.0 6048 0.0364 0.9310 0.9310 0.9310 116 0.9367 0.9367 0.9367 158 0.984 0.9919 0.9880 124 0.9499 0.9523 0.9511 0.9896
0.0365 64.0 6144 0.0360 0.9153 0.9310 0.9231 116 0.9412 0.9114 0.9260 158 0.984 0.9919 0.9880 124 0.9470 0.9422 0.9446 0.9887
0.0347 65.0 6240 0.0354 0.9237 0.9397 0.9316 116 0.9416 0.9177 0.9295 158 0.984 0.9919 0.9880 124 0.9496 0.9472 0.9484 0.9887
0.0393 66.0 6336 0.0366 0.9397 0.9397 0.9397 116 0.9355 0.9177 0.9265 158 0.984 0.9919 0.9880 124 0.9520 0.9472 0.9496 0.9887
0.0359 67.0 6432 0.0348 0.9316 0.9397 0.9356 116 0.9241 0.9241 0.9241 158 0.984 0.9919 0.9880 124 0.945 0.9497 0.9474 0.9893
0.0331 68.0 6528 0.0347 0.9316 0.9397 0.9356 116 0.9177 0.9177 0.9177 158 0.984 0.9919 0.9880 124 0.9425 0.9472 0.9449 0.9890
0.0344 69.0 6624 0.0341 0.9391 0.9310 0.9351 116 0.9363 0.9304 0.9333 158 0.984 0.9919 0.9880 124 0.9521 0.9497 0.9509 0.9898
0.0349 70.0 6720 0.0345 0.9397 0.9397 0.9397 116 0.9427 0.9367 0.9397 158 0.984 0.9919 0.9880 124 0.9548 0.9548 0.9548 0.9901
0.0349 71.0 6816 0.0354 0.9310 0.9310 0.9310 116 0.9299 0.9241 0.9270 158 0.984 0.9919 0.9880 124 0.9472 0.9472 0.9472 0.9885
0.0342 72.0 6912 0.0343 0.9237 0.9397 0.9316 116 0.9299 0.9241 0.9270 158 0.984 0.9919 0.9880 124 0.945 0.9497 0.9474 0.9887
0.0333 73.0 7008 0.0354 0.9391 0.9310 0.9351 116 0.9241 0.9241 0.9241 158 0.984 0.9919 0.9880 124 0.9472 0.9472 0.9472 0.9890
0.0332 74.0 7104 0.0346 0.9231 0.9310 0.9270 116 0.9241 0.9241 0.9241 158 0.984 0.9919 0.9880 124 0.9425 0.9472 0.9449 0.9893
0.0346 75.0 7200 0.0342 0.9310 0.9310 0.9310 116 0.9245 0.9304 0.9274 158 0.984 0.9919 0.9880 124 0.945 0.9497 0.9474 0.9896
0.0334 76.0 7296 0.0346 0.9224 0.9224 0.9224 116 0.925 0.9367 0.9308 158 0.984 0.9919 0.9880 124 0.9426 0.9497 0.9462 0.9904
0.034 77.0 7392 0.0350 0.9397 0.9397 0.9397 116 0.9299 0.9241 0.9270 158 0.984 0.9919 0.9880 124 0.9497 0.9497 0.9497 0.9896
0.0341 78.0 7488 0.0340 0.9316 0.9397 0.9356 116 0.9363 0.9304 0.9333 158 0.984 0.9919 0.9880 124 0.9499 0.9523 0.9511 0.9904
0.033 79.0 7584 0.0348 0.9304 0.9224 0.9264 116 0.925 0.9367 0.9308 158 0.984 0.9919 0.9880 124 0.945 0.9497 0.9474 0.9896
0.0308 80.0 7680 0.0337 0.9138 0.9138 0.9138 116 0.9193 0.9367 0.9279 158 0.984 0.9919 0.9880 124 0.9378 0.9472 0.9425 0.9898
0.031 81.0 7776 0.0341 0.9224 0.9224 0.9224 116 0.9193 0.9367 0.9279 158 0.984 0.9919 0.9880 124 0.9403 0.9497 0.9450 0.9901
0.0315 82.0 7872 0.0340 0.9237 0.9397 0.9316 116 0.9363 0.9304 0.9333 158 0.984 0.9919 0.9880 124 0.9475 0.9523 0.9499 0.9904
0.0321 83.0 7968 0.0343 0.9391 0.9310 0.9351 116 0.9367 0.9367 0.9367 158 0.984 0.9919 0.9880 124 0.9523 0.9523 0.9523 0.9901
0.0317 84.0 8064 0.0340 0.9391 0.9310 0.9351 116 0.9367 0.9367 0.9367 158 0.984 0.9919 0.9880 124 0.9523 0.9523 0.9523 0.9901
0.0324 85.0 8160 0.0340 0.9145 0.9224 0.9185 116 0.9187 0.9304 0.9245 158 0.984 0.9919 0.9880 124 0.9378 0.9472 0.9425 0.9893
0.0317 86.0 8256 0.0339 0.9316 0.9397 0.9356 116 0.9423 0.9304 0.9363 158 0.984 0.9919 0.9880 124 0.9523 0.9523 0.9523 0.9901
0.0308 87.0 8352 0.0347 0.9316 0.9397 0.9356 116 0.9423 0.9304 0.9363 158 0.984 0.9919 0.9880 124 0.9523 0.9523 0.9523 0.9898
0.0311 88.0 8448 0.0344 0.9391 0.9310 0.9351 116 0.9367 0.9367 0.9367 158 0.984 0.9919 0.9880 124 0.9523 0.9523 0.9523 0.9898
0.0295 89.0 8544 0.0346 0.9391 0.9310 0.9351 116 0.9427 0.9367 0.9397 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9896
0.0304 90.0 8640 0.0343 0.9391 0.9310 0.9351 116 0.9427 0.9367 0.9397 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9896
0.0315 91.0 8736 0.0343 0.9391 0.9310 0.9351 116 0.9427 0.9367 0.9397 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9896
0.0314 92.0 8832 0.0342 0.9391 0.9310 0.9351 116 0.9427 0.9367 0.9397 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9896
0.0322 93.0 8928 0.0340 0.9391 0.9310 0.9351 116 0.9427 0.9367 0.9397 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9898
0.0303 94.0 9024 0.0343 0.9391 0.9310 0.9351 116 0.9367 0.9367 0.9367 158 0.984 0.9919 0.9880 124 0.9523 0.9523 0.9523 0.9898
0.0316 95.0 9120 0.0343 0.9391 0.9310 0.9351 116 0.9367 0.9367 0.9367 158 0.984 0.9919 0.9880 124 0.9523 0.9523 0.9523 0.9898
0.0317 96.0 9216 0.0342 0.9391 0.9310 0.9351 116 0.9427 0.9367 0.9397 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9896
0.0321 97.0 9312 0.0341 0.9316 0.9397 0.9356 116 0.9484 0.9304 0.9393 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9898
0.0295 98.0 9408 0.0342 0.9316 0.9397 0.9356 116 0.9484 0.9304 0.9393 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9898
0.031 99.0 9504 0.0341 0.9316 0.9397 0.9356 116 0.9484 0.9304 0.9393 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9898
0.0299 100.0 9600 0.0342 0.9316 0.9397 0.9356 116 0.9484 0.9304 0.9393 158 0.984 0.9919 0.9880 124 0.9547 0.9523 0.9535 0.9896

Framework versions

  • Transformers 4.39.3
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.15.2