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sembr2023-distilbert-base-uncased

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distilbert
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metadata
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: sembr2023-distilbert-base-uncased
    results: []

sembr2023-distilbert-base-uncased

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

  • Loss: 0.2391
  • Precision: 0.8025
  • Recall: 0.8289
  • F1: 0.8155
  • Iou: 0.6885
  • Accuracy: 0.9655
  • Balanced Accuracy: 0.9041
  • Overall Accuracy: 0.9498

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: 0.0001
  • train_batch_size: 64
  • eval_batch_size: 128
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • training_steps: 1000

Training results

Training Loss Epoch Step Validation Loss Precision Recall F1 Iou Accuracy Balanced Accuracy Overall Accuracy
0.4046 0.06 10 0.4081 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.376 0.12 20 0.3776 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.3291 0.18 30 0.2954 0.8417 0.0813 0.1482 0.0801 0.9141 0.5399 0.9134
0.2309 0.24 40 0.2165 0.8158 0.6008 0.6920 0.5290 0.9508 0.7935 0.9357
0.1704 0.3 50 0.1928 0.8703 0.6428 0.7394 0.5866 0.9583 0.8166 0.9420
0.1711 0.36 60 0.1810 0.7918 0.7620 0.7766 0.6348 0.9597 0.8709 0.9404
0.1716 0.42 70 0.1841 0.8143 0.7735 0.7934 0.6575 0.9629 0.8778 0.9426
0.1452 0.48 80 0.1610 0.9013 0.6827 0.7769 0.6352 0.9639 0.8376 0.9486
0.1233 0.55 90 0.1604 0.8569 0.7587 0.8048 0.6734 0.9662 0.8729 0.9487
0.1291 0.61 100 0.1694 0.8405 0.7794 0.8088 0.6789 0.9661 0.8822 0.9475
0.1335 0.67 110 0.1529 0.8730 0.7445 0.8036 0.6717 0.9665 0.8667 0.9494
0.0921 0.73 120 0.1675 0.8238 0.8090 0.8164 0.6897 0.9665 0.8958 0.9462
0.0936 0.79 130 0.1469 0.8672 0.7738 0.8178 0.6918 0.9683 0.8809 0.9520
0.1168 0.85 140 0.1570 0.8190 0.8088 0.8139 0.6862 0.9660 0.8953 0.9483
0.091 0.91 150 0.1630 0.8330 0.7842 0.8079 0.6777 0.9657 0.8841 0.9477
0.0795 0.97 160 0.1413 0.8477 0.7934 0.8196 0.6944 0.9679 0.8895 0.9539
0.0927 1.03 170 0.1678 0.8071 0.8218 0.8144 0.6869 0.9655 0.9009 0.9467
0.0748 1.09 180 0.1346 0.8785 0.7628 0.8166 0.6900 0.9685 0.8761 0.9563
0.0731 1.15 190 0.1691 0.8290 0.8018 0.8152 0.6880 0.9666 0.8925 0.9485
0.0842 1.21 200 0.1561 0.8264 0.8127 0.8195 0.6942 0.9671 0.8977 0.9512
0.0702 1.27 210 0.1419 0.8459 0.7931 0.8187 0.6930 0.9677 0.8892 0.9539
0.0577 1.33 220 0.1506 0.8375 0.8057 0.8213 0.6968 0.9678 0.8950 0.9521
0.0596 1.39 230 0.1628 0.7907 0.8377 0.8135 0.6856 0.9647 0.9076 0.9485
0.0562 1.45 240 0.1585 0.8413 0.7953 0.8176 0.6915 0.9674 0.8900 0.9518
0.0519 1.52 250 0.1740 0.8062 0.8189 0.8125 0.6842 0.9652 0.8995 0.9490
0.0547 1.58 260 0.1751 0.8593 0.7752 0.8151 0.6879 0.9677 0.8812 0.9521
0.0604 1.64 270 0.1693 0.8155 0.8116 0.8136 0.6857 0.9658 0.8965 0.9503
0.043 1.7 280 0.1939 0.8106 0.8264 0.8184 0.6926 0.9663 0.9034 0.9490
0.0538 1.76 290 0.1800 0.8320 0.8146 0.8232 0.6996 0.9678 0.8990 0.9515
0.0557 1.82 300 0.1762 0.7867 0.8289 0.8073 0.6768 0.9636 0.9031 0.9473
0.0493 1.88 310 0.1646 0.8411 0.7894 0.8144 0.6870 0.9669 0.8872 0.9519
0.0524 1.94 320 0.1674 0.8155 0.8205 0.8180 0.6921 0.9664 0.9009 0.9507
0.0445 2.0 330 0.1616 0.8186 0.8075 0.8130 0.6849 0.9658 0.8947 0.9511
0.0399 2.06 340 0.2141 0.7818 0.8352 0.8076 0.6773 0.9634 0.9058 0.9455
0.039 2.12 350 0.1765 0.8189 0.8134 0.8161 0.6894 0.9663 0.8976 0.9509
0.0353 2.18 360 0.1900 0.8097 0.8130 0.8113 0.6826 0.9652 0.8968 0.9495
0.0269 2.24 370 0.2080 0.8338 0.7953 0.8141 0.6865 0.9666 0.8896 0.9508
0.0333 2.3 380 0.2032 0.7753 0.8341 0.8036 0.6717 0.9625 0.9048 0.9466
0.0339 2.36 390 0.1867 0.8309 0.8045 0.8175 0.6913 0.9670 0.8939 0.9520
0.035 2.42 400 0.1826 0.8078 0.8213 0.8145 0.6870 0.9656 0.9007 0.9500
0.039 2.48 410 0.2028 0.7762 0.8335 0.8038 0.6720 0.9626 0.9046 0.9466
0.0303 2.55 420 0.1964 0.8096 0.8153 0.8124 0.6841 0.9654 0.8979 0.9495
0.0296 2.61 430 0.1908 0.7883 0.8349 0.8109 0.6820 0.9642 0.9061 0.9485
0.0356 2.67 440 0.2016 0.8151 0.8140 0.8145 0.6871 0.9659 0.8977 0.9497
0.0343 2.73 450 0.1963 0.8016 0.8248 0.8130 0.6850 0.9651 0.9021 0.9497
0.0271 2.79 460 0.2077 0.8172 0.8127 0.8150 0.6877 0.9661 0.8972 0.9500
0.0311 2.85 470 0.1889 0.8080 0.8254 0.8166 0.6900 0.9659 0.9027 0.9512
0.029 2.91 480 0.1995 0.7698 0.8443 0.8053 0.6741 0.9625 0.9094 0.9469
0.0265 2.97 490 0.1828 0.8245 0.8148 0.8196 0.6944 0.9670 0.8986 0.9528
0.0281 3.03 500 0.2061 0.8321 0.8070 0.8194 0.6940 0.9673 0.8953 0.9520
0.0273 3.09 510 0.2072 0.8105 0.8218 0.8161 0.6894 0.9659 0.9012 0.9502
0.0268 3.15 520 0.2113 0.8084 0.8222 0.8152 0.6881 0.9657 0.9012 0.9502
0.0241 3.21 530 0.2015 0.8318 0.8038 0.8176 0.6914 0.9670 0.8937 0.9524
0.0198 3.27 540 0.2252 0.7922 0.8331 0.8122 0.6837 0.9646 0.9055 0.9483
0.0268 3.33 550 0.2107 0.7931 0.8343 0.8132 0.6852 0.9647 0.9061 0.9493
0.0185 3.39 560 0.2202 0.7874 0.8311 0.8086 0.6787 0.9638 0.9042 0.9485
0.0224 3.45 570 0.2256 0.8057 0.8247 0.8151 0.6879 0.9656 0.9023 0.9497
0.0189 3.52 580 0.2125 0.8111 0.8204 0.8157 0.6888 0.9659 0.9005 0.9504
0.026 3.58 590 0.2163 0.8122 0.8182 0.8152 0.6881 0.9659 0.8995 0.9506
0.0184 3.64 600 0.2143 0.8057 0.8204 0.8130 0.6849 0.9653 0.9002 0.9502
0.0196 3.7 610 0.2113 0.8171 0.8164 0.8167 0.6903 0.9663 0.8990 0.9509
0.0205 3.76 620 0.2081 0.8127 0.8213 0.8170 0.6906 0.9662 0.9011 0.9512
0.0231 3.82 630 0.2200 0.8170 0.8225 0.8198 0.6946 0.9667 0.9019 0.9508
0.0209 3.88 640 0.2179 0.8148 0.8261 0.8204 0.6955 0.9667 0.9035 0.9512
0.0164 3.94 650 0.2201 0.8338 0.8124 0.8229 0.6992 0.9679 0.8980 0.9522
0.0222 4.0 660 0.2174 0.8193 0.8124 0.8158 0.6890 0.9663 0.8971 0.9505
0.0209 4.06 670 0.2169 0.7975 0.8336 0.8151 0.6880 0.9652 0.9061 0.9502
0.0165 4.12 680 0.2248 0.7973 0.8317 0.8141 0.6865 0.9651 0.9052 0.9494
0.0136 4.18 690 0.2254 0.8073 0.8245 0.8158 0.6889 0.9658 0.9023 0.9501
0.016 4.24 700 0.2259 0.8029 0.8231 0.8129 0.6847 0.9651 0.9013 0.9499
0.018 4.3 710 0.2291 0.8013 0.8264 0.8136 0.6858 0.9652 0.9028 0.9496
0.0148 4.36 720 0.2316 0.7923 0.8283 0.8099 0.6806 0.9642 0.9031 0.9483
0.0163 4.42 730 0.2423 0.7882 0.8321 0.8096 0.6801 0.9640 0.9047 0.9475
0.0162 4.48 740 0.2312 0.8042 0.8243 0.8141 0.6865 0.9654 0.9020 0.9496
0.0121 4.55 750 0.2355 0.8003 0.8257 0.8128 0.6847 0.9650 0.9024 0.9494
0.0182 4.61 760 0.2393 0.7911 0.8313 0.8107 0.6817 0.9643 0.9046 0.9485
0.0182 4.67 770 0.2292 0.7954 0.8308 0.8127 0.6845 0.9648 0.9046 0.9494
0.0153 4.73 780 0.2312 0.8043 0.8266 0.8153 0.6882 0.9656 0.9031 0.9498
0.0182 4.79 790 0.2410 0.7953 0.8326 0.8135 0.6857 0.9649 0.9055 0.9489
0.0133 4.85 800 0.2350 0.8110 0.8219 0.8164 0.6898 0.9660 0.9013 0.9505
0.0185 4.91 810 0.2432 0.7912 0.8363 0.8132 0.6851 0.9647 0.9070 0.9486
0.0157 4.97 820 0.2318 0.8103 0.8214 0.8158 0.6889 0.9659 0.9010 0.9503
0.0171 5.03 830 0.2392 0.7992 0.8294 0.8140 0.6864 0.9651 0.9042 0.9492
0.0121 5.09 840 0.2422 0.8043 0.8271 0.8155 0.6885 0.9656 0.9034 0.9497
0.0103 5.15 850 0.2382 0.8009 0.8296 0.8150 0.6877 0.9654 0.9043 0.9495
0.0148 5.21 860 0.2358 0.8057 0.8256 0.8155 0.6885 0.9656 0.9027 0.9501
0.0115 5.27 870 0.2398 0.8008 0.8290 0.8147 0.6873 0.9653 0.9041 0.9497
0.0115 5.33 880 0.2385 0.8035 0.8245 0.8138 0.6861 0.9653 0.9020 0.9496
0.0122 5.39 890 0.2387 0.7983 0.8303 0.8140 0.6863 0.9651 0.9045 0.9494
0.0134 5.45 900 0.2395 0.8009 0.8279 0.8142 0.6866 0.9652 0.9035 0.9495
0.0169 5.52 910 0.2391 0.8012 0.8282 0.8145 0.6870 0.9653 0.9037 0.9496
0.0178 5.58 920 0.2403 0.7997 0.8290 0.8141 0.6865 0.9652 0.9040 0.9493
0.0162 5.64 930 0.2391 0.8017 0.8274 0.8143 0.6868 0.9653 0.9033 0.9496
0.0115 5.7 940 0.2384 0.8035 0.8265 0.8148 0.6875 0.9655 0.9030 0.9498
0.0175 5.76 950 0.2386 0.8029 0.8282 0.8153 0.6882 0.9655 0.9038 0.9498
0.0165 5.82 960 0.2390 0.8017 0.8288 0.8150 0.6878 0.9654 0.9040 0.9497
0.0186 5.88 970 0.2389 0.8021 0.8289 0.8153 0.6882 0.9655 0.9041 0.9498
0.0124 5.94 980 0.2390 0.8024 0.8288 0.8154 0.6883 0.9655 0.9041 0.9498
0.0138 6.0 990 0.2391 0.8025 0.8289 0.8155 0.6885 0.9655 0.9041 0.9498
0.0113 6.06 1000 0.2391 0.8025 0.8289 0.8155 0.6885 0.9655 0.9041 0.9498

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.0.1
  • Datasets 2.14.6
  • Tokenizers 0.14.1