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---
license: mit
base_model: prajjwal1/bert-mini
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: sembr2023-bert-mini
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sembr2023-bert-mini
This model is a fine-tuned version of [prajjwal1/bert-mini](https://huggingface.co/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
|