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sembr2023-bert-mini

Token Classification
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PyTorch
bert
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metadata
license: mit
base_model: prajjwal1/bert-mini
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: sembr2023-bert-mini
    results: []

sembr2023-bert-mini

This model is a fine-tuned version of prajjwal1/bert-mini on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1844
  • Precision: 0.7925
  • Recall: 0.7950
  • F1: 0.7938
  • Iou: 0.6581
  • Accuracy: 0.9620
  • Balanced Accuracy: 0.8870
  • Overall Accuracy: 0.9443

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
0.5028 0.06 10 0.5036 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.417 0.12 20 0.4416 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.4292 0.18 30 0.4298 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.3964 0.24 40 0.4121 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.3611 0.3 50 0.3619 0.5833 0.0080 0.0158 0.0080 0.9082 0.5037 0.9081
0.3201 0.36 60 0.3103 0.6414 0.5085 0.5673 0.3960 0.9287 0.7399 0.9146
0.2983 0.42 70 0.2575 0.7866 0.6423 0.7072 0.5470 0.9511 0.8123 0.9333
0.2621 0.48 80 0.2402 0.7879 0.6824 0.7314 0.5765 0.9539 0.8319 0.9354
0.2464 0.55 90 0.2262 0.8304 0.6660 0.7391 0.5862 0.9568 0.8261 0.9384
0.21 0.61 100 0.2186 0.7990 0.7131 0.7536 0.6047 0.9571 0.8475 0.9382
0.2238 0.67 110 0.2042 0.8242 0.6860 0.7487 0.5984 0.9577 0.8356 0.9408
0.1849 0.73 120 0.1990 0.8799 0.6422 0.7425 0.5904 0.9590 0.8166 0.9432
0.1702 0.79 130 0.1959 0.7862 0.7436 0.7643 0.6185 0.9578 0.8615 0.9392
0.2033 0.85 140 0.1939 0.7996 0.7414 0.7694 0.6252 0.9591 0.8613 0.9404
0.1686 0.91 150 0.1916 0.7982 0.7437 0.7700 0.6260 0.9591 0.8623 0.9410
0.1484 0.97 160 0.1855 0.7898 0.7597 0.7745 0.6320 0.9593 0.8696 0.9408
0.1717 1.03 170 0.1864 0.8231 0.7301 0.7738 0.6310 0.9607 0.8571 0.9434
0.1584 1.09 180 0.1844 0.7919 0.7676 0.7796 0.6388 0.9601 0.8736 0.9414
0.1455 1.15 190 0.1807 0.8255 0.7496 0.7857 0.6470 0.9624 0.8668 0.9437
0.1521 1.21 200 0.1746 0.8277 0.7428 0.7829 0.6433 0.9621 0.8636 0.9450
0.1385 1.27 210 0.1833 0.8099 0.7573 0.7828 0.6431 0.9613 0.8697 0.9430
0.1246 1.33 220 0.1857 0.7400 0.8164 0.7763 0.6344 0.9567 0.8937 0.9366
0.1312 1.39 230 0.1738 0.8345 0.7441 0.7867 0.6484 0.9629 0.8646 0.9458
0.1139 1.45 240 0.1750 0.7964 0.7861 0.7912 0.6545 0.9618 0.8829 0.9428
0.1181 1.52 250 0.1728 0.8201 0.7578 0.7877 0.6498 0.9624 0.8705 0.9448
0.1248 1.58 260 0.1774 0.8148 0.7727 0.7932 0.6573 0.9629 0.8775 0.9441
0.1384 1.64 270 0.1748 0.7840 0.7925 0.7882 0.6505 0.9608 0.8852 0.9418
0.1068 1.7 280 0.1744 0.7943 0.7950 0.7947 0.6593 0.9622 0.8871 0.9430
0.114 1.76 290 0.1749 0.7916 0.7904 0.7910 0.6543 0.9616 0.8847 0.9428
0.1214 1.82 300 0.1778 0.7551 0.8233 0.7877 0.6498 0.9592 0.8981 0.9392
0.1139 1.88 310 0.1764 0.7897 0.7839 0.7868 0.6485 0.9609 0.8814 0.9425
0.1254 1.94 320 0.1771 0.7908 0.7925 0.7916 0.6551 0.9616 0.8856 0.9427
0.1001 2.0 330 0.1715 0.8057 0.7829 0.7942 0.6586 0.9627 0.8819 0.9445
0.0989 2.06 340 0.1705 0.8099 0.7803 0.7948 0.6595 0.9629 0.8809 0.9446
0.1222 2.12 350 0.1761 0.7843 0.7991 0.7916 0.6551 0.9613 0.8884 0.9422
0.1032 2.18 360 0.1754 0.7961 0.7864 0.7912 0.6545 0.9618 0.8830 0.9432
0.0799 2.24 370 0.1753 0.7867 0.7879 0.7873 0.6492 0.9608 0.8831 0.9432
0.099 2.3 380 0.1751 0.8101 0.7738 0.7915 0.6549 0.9625 0.8777 0.9451
0.0993 2.36 390 0.1699 0.8073 0.7791 0.7929 0.6569 0.9626 0.8801 0.9454
0.1025 2.42 400 0.1662 0.8203 0.7764 0.7978 0.6636 0.9638 0.8796 0.9465
0.1081 2.48 410 0.1762 0.8005 0.7893 0.7949 0.6596 0.9625 0.8847 0.9444
0.1118 2.55 420 0.1720 0.8130 0.7755 0.7938 0.6582 0.9630 0.8787 0.9458
0.0779 2.61 430 0.1712 0.8131 0.7797 0.7961 0.6612 0.9633 0.8808 0.9454
0.0944 2.67 440 0.1788 0.7754 0.8094 0.7921 0.6557 0.9609 0.8928 0.9419
0.1053 2.73 450 0.1696 0.7980 0.7901 0.7940 0.6584 0.9623 0.8849 0.9450
0.0889 2.79 460 0.1719 0.8215 0.7736 0.7968 0.6623 0.9637 0.8783 0.9465
0.0879 2.85 470 0.1712 0.8091 0.7828 0.7957 0.6608 0.9630 0.8820 0.9457
0.0867 2.91 480 0.1769 0.8021 0.78 0.7909 0.6541 0.9621 0.8803 0.9447
0.0787 2.97 490 0.1788 0.8044 0.7831 0.7936 0.6578 0.9625 0.8819 0.9447
0.0945 3.03 500 0.1736 0.8055 0.7820 0.7936 0.6578 0.9626 0.8815 0.9445
0.1011 3.09 510 0.1823 0.7881 0.7962 0.7921 0.6558 0.9616 0.8873 0.9432
0.0914 3.15 520 0.1819 0.7958 0.7939 0.7948 0.6595 0.9623 0.8866 0.9438
0.0837 3.21 530 0.1738 0.8129 0.7857 0.7991 0.6654 0.9637 0.8837 0.9460
0.0776 3.27 540 0.1828 0.7921 0.7961 0.7941 0.6585 0.9620 0.8874 0.9437
0.0916 3.33 550 0.1776 0.7835 0.7994 0.7913 0.6547 0.9612 0.8885 0.9433
0.081 3.39 560 0.1784 0.7784 0.8033 0.7907 0.6538 0.9609 0.8901 0.9428
0.0867 3.45 570 0.1793 0.7728 0.8074 0.7897 0.6525 0.9605 0.8917 0.9425
0.0816 3.52 580 0.1789 0.7829 0.8017 0.7922 0.6559 0.9613 0.8896 0.9433
0.0808 3.58 590 0.1791 0.7890 0.7941 0.7916 0.6550 0.9615 0.8863 0.9435
0.07 3.64 600 0.1844 0.7697 0.8071 0.7880 0.6501 0.9600 0.8913 0.9420
0.0775 3.7 610 0.1795 0.7849 0.7957 0.7902 0.6532 0.9612 0.8868 0.9433
0.0722 3.76 620 0.1772 0.7993 0.7814 0.7903 0.6532 0.9619 0.8808 0.9449
0.0786 3.82 630 0.1775 0.8159 0.7763 0.7956 0.6606 0.9633 0.8793 0.9457
0.0768 3.88 640 0.1823 0.8015 0.7848 0.7931 0.6571 0.9623 0.8826 0.9442
0.0728 3.94 650 0.1806 0.7918 0.7885 0.7901 0.6531 0.9615 0.8838 0.9438
0.0762 4.0 660 0.1831 0.7881 0.7935 0.7908 0.6540 0.9614 0.8859 0.9435
0.0776 4.06 670 0.1788 0.8015 0.7847 0.7930 0.6570 0.9623 0.8825 0.9453
0.0843 4.12 680 0.1824 0.8009 0.7876 0.7942 0.6587 0.9625 0.8839 0.9445
0.066 4.18 690 0.1843 0.7921 0.7918 0.7920 0.6556 0.9617 0.8854 0.9440
0.0832 4.24 700 0.1781 0.7957 0.7893 0.7925 0.6563 0.9620 0.8844 0.9447
0.0761 4.3 710 0.1871 0.7817 0.8017 0.7916 0.6550 0.9612 0.8895 0.9428
0.0696 4.36 720 0.1813 0.7957 0.7924 0.7940 0.6584 0.9622 0.8859 0.9446
0.0734 4.42 730 0.1827 0.7934 0.7938 0.7936 0.6578 0.9620 0.8864 0.9444
0.0823 4.48 740 0.1856 0.7956 0.7913 0.7935 0.6576 0.9621 0.8854 0.9443
0.0662 4.55 750 0.1790 0.7890 0.7952 0.7921 0.6557 0.9616 0.8868 0.9444
0.0775 4.61 760 0.1858 0.7899 0.7953 0.7926 0.6564 0.9617 0.8869 0.9439
0.0764 4.67 770 0.1853 0.7852 0.8011 0.7931 0.6572 0.9616 0.8895 0.9436
0.0689 4.73 780 0.1804 0.7964 0.7924 0.7944 0.6589 0.9623 0.8859 0.9445
0.0785 4.79 790 0.1817 0.7937 0.7921 0.7929 0.6569 0.9619 0.8856 0.9444
0.075 4.85 800 0.1856 0.7912 0.7929 0.7920 0.6556 0.9617 0.8858 0.9440
0.0691 4.91 810 0.1844 0.7805 0.8 0.7901 0.6531 0.9609 0.8886 0.9432
0.0835 4.97 820 0.1829 0.7984 0.7911 0.7947 0.6594 0.9624 0.8854 0.9448
0.0712 5.03 830 0.1820 0.7906 0.7927 0.7917 0.6552 0.9616 0.8857 0.9443
0.0594 5.09 840 0.1841 0.7902 0.7948 0.7925 0.6563 0.9617 0.8867 0.9441
0.0775 5.15 850 0.1834 0.7927 0.7936 0.7932 0.6572 0.9619 0.8863 0.9444
0.0755 5.21 860 0.1833 0.7924 0.7944 0.7934 0.6575 0.9619 0.8867 0.9444
0.0717 5.27 870 0.1838 0.7902 0.7958 0.7930 0.6570 0.9618 0.8872 0.9443
0.0694 5.33 880 0.1834 0.7918 0.7939 0.7928 0.6568 0.9618 0.8864 0.9444
0.0759 5.39 890 0.1826 0.7905 0.7954 0.7929 0.6569 0.9618 0.8870 0.9443
0.0666 5.45 900 0.1821 0.7945 0.7922 0.7933 0.6575 0.9620 0.8857 0.9446
0.08 5.52 910 0.1829 0.7924 0.7953 0.7938 0.6581 0.9620 0.8871 0.9444
0.0816 5.58 920 0.1837 0.7918 0.7954 0.7936 0.6578 0.9619 0.8871 0.9443
0.0762 5.64 930 0.1837 0.7922 0.7954 0.7938 0.6581 0.9620 0.8871 0.9443
0.0655 5.7 940 0.1843 0.7906 0.7962 0.7934 0.6575 0.9619 0.8874 0.9442
0.0737 5.76 950 0.1846 0.7904 0.7964 0.7934 0.6576 0.9619 0.8875 0.9441
0.0717 5.82 960 0.1846 0.7905 0.7961 0.7933 0.6574 0.9618 0.8873 0.9441
0.0829 5.88 970 0.1845 0.7917 0.7954 0.7935 0.6577 0.9619 0.8871 0.9443
0.0766 5.94 980 0.1844 0.7924 0.7952 0.7938 0.6581 0.9620 0.8870 0.9443
0.0704 6.0 990 0.1844 0.7925 0.7950 0.7938 0.6581 0.9620 0.8870 0.9443
0.0755 6.06 1000 0.1844 0.7925 0.7950 0.7938 0.6581 0.9620 0.8870 0.9443

Framework versions

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