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sembr2023-distilbert-base-cased

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

sembr2023-distilbert-base-cased

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

  • Loss: 0.2214
  • Precision: 0.7952
  • Recall: 0.8261
  • F1: 0.8104
  • Iou: 0.6812
  • Accuracy: 0.9665
  • Balanced Accuracy: 0.9030
  • Overall Accuracy: 0.9525

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
0.3717 0.06 10 0.3886 0 0.0 0.0 0.0 0.9133 0.5 0.9133
0.3692 0.12 20 0.3552 0 0.0 0.0 0.0 0.9133 0.5 0.9133
0.2579 0.17 30 0.2734 0.8923 0.1841 0.3052 0.1801 0.9274 0.5910 0.9255
0.2135 0.23 40 0.2224 0.7632 0.6267 0.6882 0.5247 0.9508 0.8041 0.9348
0.225 0.29 50 0.1935 0.8426 0.6378 0.7260 0.5699 0.9583 0.8132 0.9427
0.1637 0.35 60 0.1785 0.8115 0.6951 0.7488 0.5985 0.9596 0.8399 0.9470
0.1497 0.4 70 0.1837 0.8159 0.6961 0.7513 0.6016 0.9601 0.8406 0.9434
0.1248 0.46 80 0.1758 0.7920 0.7523 0.7717 0.6282 0.9614 0.8668 0.9446
0.1297 0.52 90 0.1796 0.7740 0.7933 0.7835 0.6441 0.9620 0.8857 0.9430
0.1321 0.58 100 0.1721 0.8616 0.7178 0.7832 0.6436 0.9656 0.8534 0.9496
0.1058 0.64 110 0.1572 0.8132 0.7766 0.7945 0.6591 0.9652 0.8799 0.9494
0.1183 0.69 120 0.1734 0.8084 0.7792 0.7935 0.6578 0.9649 0.8809 0.9470
0.1195 0.75 130 0.1652 0.7753 0.7952 0.7851 0.6462 0.9623 0.8867 0.9463
0.0996 0.81 140 0.1433 0.8292 0.7684 0.7977 0.6634 0.9662 0.8767 0.9527
0.1009 0.87 150 0.1817 0.8181 0.7808 0.7990 0.6653 0.9660 0.8822 0.9480
0.0953 0.92 160 0.1554 0.8669 0.7245 0.7893 0.6519 0.9665 0.8570 0.9524
0.1077 0.98 170 0.1556 0.8261 0.7752 0.7998 0.6664 0.9664 0.8798 0.9512
0.0981 1.04 180 0.1526 0.8283 0.7703 0.7982 0.6642 0.9663 0.8776 0.9520
0.0982 1.1 190 0.1547 0.8001 0.7976 0.7989 0.6651 0.9652 0.8894 0.9504
0.0789 1.16 200 0.1606 0.8135 0.7947 0.8040 0.6722 0.9664 0.8887 0.9513
0.0829 1.21 210 0.1566 0.8244 0.7872 0.8054 0.6741 0.9670 0.8856 0.9517
0.0742 1.27 220 0.1680 0.8167 0.7895 0.8029 0.6707 0.9664 0.8864 0.9506
0.084 1.33 230 0.1680 0.8197 0.7824 0.8006 0.6675 0.9662 0.8830 0.9511
0.0702 1.39 240 0.1653 0.8184 0.7996 0.8089 0.6791 0.9673 0.8914 0.9510
0.0713 1.45 250 0.1675 0.7844 0.8184 0.8010 0.6681 0.9648 0.8985 0.9492
0.0763 1.5 260 0.1501 0.8239 0.7833 0.8031 0.6710 0.9667 0.8837 0.9532
0.0738 1.56 270 0.1518 0.8203 0.7962 0.8081 0.6780 0.9672 0.8898 0.9527
0.0736 1.62 280 0.1624 0.7849 0.8222 0.8031 0.6710 0.9651 0.9004 0.9508
0.0659 1.68 290 0.1735 0.7775 0.8308 0.8033 0.6712 0.9647 0.9041 0.9496
0.0653 1.73 300 0.1586 0.7828 0.8224 0.8022 0.6697 0.9648 0.9004 0.9503
0.0635 1.79 310 0.1720 0.8091 0.8033 0.8062 0.6753 0.9665 0.8927 0.9510
0.0724 1.85 320 0.1588 0.8057 0.8033 0.8045 0.6729 0.9662 0.8925 0.9531
0.0612 1.91 330 0.1818 0.7828 0.8222 0.8020 0.6695 0.9648 0.9003 0.9488
0.0612 1.97 340 0.1704 0.8235 0.7893 0.8060 0.6751 0.9671 0.8866 0.9526
0.0592 2.02 350 0.1634 0.8002 0.7929 0.7965 0.6618 0.9649 0.8870 0.9520
0.0474 2.08 360 0.1835 0.7931 0.8120 0.8025 0.6701 0.9654 0.8960 0.9506
0.0484 2.14 370 0.1790 0.8123 0.7941 0.8031 0.6710 0.9663 0.8883 0.9522
0.0524 2.2 380 0.1812 0.7702 0.8291 0.7985 0.6646 0.9637 0.9028 0.9499
0.052 2.25 390 0.1716 0.8041 0.7964 0.8002 0.6670 0.9655 0.8890 0.9533
0.0443 2.31 400 0.1676 0.8054 0.7976 0.8015 0.6687 0.9658 0.8897 0.9535
0.057 2.37 410 0.1836 0.8028 0.8084 0.8056 0.6745 0.9662 0.8948 0.9507
0.0414 2.43 420 0.1791 0.8049 0.8053 0.8051 0.6737 0.9662 0.8934 0.9527
0.0471 2.49 430 0.1771 0.7964 0.8126 0.8044 0.6728 0.9658 0.8965 0.9527
0.039 2.54 440 0.1773 0.8066 0.8021 0.8043 0.6727 0.9662 0.8919 0.9537
0.0543 2.6 450 0.1855 0.7887 0.8193 0.8037 0.6718 0.9653 0.8992 0.9511
0.0398 2.66 460 0.1959 0.7938 0.8147 0.8041 0.6724 0.9656 0.8973 0.9504
0.0419 2.72 470 0.1944 0.7847 0.8286 0.8060 0.6751 0.9654 0.9035 0.9498
0.0436 2.77 480 0.1869 0.8002 0.8109 0.8055 0.6744 0.9661 0.8958 0.9520
0.0497 2.83 490 0.1850 0.7736 0.8422 0.8065 0.6757 0.9650 0.9094 0.9501
0.0408 2.89 500 0.1883 0.8178 0.7962 0.8068 0.6762 0.9670 0.8897 0.9527
0.0332 2.95 510 0.1883 0.7913 0.8188 0.8048 0.6733 0.9656 0.8991 0.9516
0.0382 3.01 520 0.2008 0.7914 0.8307 0.8106 0.6815 0.9664 0.9049 0.9515
0.047 3.06 530 0.1913 0.8137 0.8013 0.8075 0.6771 0.9669 0.8920 0.9522
0.0327 3.12 540 0.1969 0.7993 0.8168 0.8080 0.6778 0.9664 0.8987 0.9518
0.0338 3.18 550 0.1989 0.7962 0.8173 0.8066 0.6759 0.9660 0.8987 0.9518
0.0332 3.24 560 0.2004 0.7999 0.8178 0.8087 0.6789 0.9665 0.8992 0.9518
0.0308 3.29 570 0.1964 0.8126 0.8092 0.8109 0.6819 0.9673 0.8957 0.9537
0.0348 3.35 580 0.2032 0.7902 0.8239 0.8067 0.6761 0.9658 0.9016 0.9515
0.0351 3.41 590 0.2064 0.7855 0.8218 0.8032 0.6712 0.9651 0.9003 0.9511
0.0301 3.47 600 0.2118 0.7872 0.8265 0.8063 0.6755 0.9656 0.9026 0.9505
0.0261 3.53 610 0.1997 0.7991 0.8194 0.8091 0.6794 0.9665 0.8999 0.9522
0.0282 3.58 620 0.1950 0.8029 0.8114 0.8071 0.6766 0.9664 0.8962 0.9527
0.0326 3.64 630 0.2038 0.7873 0.8290 0.8076 0.6773 0.9658 0.9039 0.9516
0.0353 3.7 640 0.2010 0.7930 0.8228 0.8076 0.6773 0.9660 0.9012 0.9514
0.0348 3.76 650 0.2043 0.7949 0.8243 0.8093 0.6797 0.9663 0.9021 0.9519
0.0296 3.82 660 0.2050 0.7976 0.8226 0.8099 0.6805 0.9665 0.9014 0.9529
0.0287 3.87 670 0.2158 0.7820 0.8318 0.8061 0.6752 0.9653 0.9049 0.9504
0.024 3.93 680 0.2110 0.7847 0.8294 0.8065 0.6757 0.9655 0.9039 0.9512
0.0274 3.99 690 0.2075 0.7937 0.8254 0.8092 0.6796 0.9663 0.9025 0.9523
0.0247 4.05 700 0.2130 0.7995 0.8210 0.8101 0.6808 0.9666 0.9007 0.9525
0.0202 4.1 710 0.2142 0.7955 0.8215 0.8083 0.6782 0.9662 0.9007 0.9518
0.0245 4.16 720 0.2120 0.7965 0.8195 0.8078 0.6776 0.9662 0.8998 0.9516
0.0214 4.22 730 0.2151 0.7899 0.8256 0.8074 0.6770 0.9659 0.9024 0.9515
0.0202 4.28 740 0.2145 0.7963 0.8220 0.8089 0.6792 0.9664 0.9010 0.9520
0.0257 4.34 750 0.2181 0.7960 0.8217 0.8087 0.6788 0.9663 0.9009 0.9520
0.0271 4.39 760 0.2151 0.7953 0.8232 0.8090 0.6793 0.9663 0.9015 0.9518
0.0279 4.45 770 0.2196 0.7955 0.8237 0.8094 0.6798 0.9664 0.9018 0.9521
0.0273 4.51 780 0.2194 0.7984 0.8256 0.8118 0.6832 0.9668 0.9029 0.9523
0.018 4.57 790 0.2201 0.7985 0.8247 0.8114 0.6826 0.9668 0.9025 0.9526
0.0275 4.62 800 0.2204 0.7893 0.8358 0.8119 0.6834 0.9664 0.9073 0.9519
0.0198 4.68 810 0.2160 0.7983 0.8232 0.8105 0.6814 0.9666 0.9017 0.9526
0.019 4.74 820 0.2109 0.7961 0.8243 0.8100 0.6806 0.9665 0.9021 0.9527
0.0236 4.8 830 0.2208 0.7956 0.8238 0.8094 0.6799 0.9664 0.9019 0.9521
0.0177 4.86 840 0.2217 0.7900 0.8301 0.8095 0.6800 0.9661 0.9046 0.9519
0.0209 4.91 850 0.2226 0.7927 0.8285 0.8102 0.6810 0.9664 0.9040 0.9522
0.0241 4.97 860 0.2215 0.7915 0.8276 0.8091 0.6794 0.9662 0.9035 0.9521
0.0211 5.03 870 0.2181 0.7957 0.8242 0.8097 0.6802 0.9664 0.9020 0.9525
0.0234 5.09 880 0.2171 0.7975 0.8224 0.8098 0.6803 0.9665 0.9013 0.9526
0.0201 5.14 890 0.2191 0.7925 0.8265 0.8092 0.6795 0.9662 0.9030 0.9523
0.0211 5.2 900 0.2175 0.7957 0.8238 0.8095 0.6799 0.9664 0.9019 0.9526
0.0234 5.26 910 0.2207 0.7913 0.8291 0.8097 0.6803 0.9662 0.9042 0.9522
0.023 5.32 920 0.2202 0.7965 0.8234 0.8098 0.6803 0.9665 0.9017 0.9524
0.0192 5.38 930 0.2203 0.7969 0.8239 0.8102 0.6809 0.9665 0.9020 0.9525
0.0217 5.43 940 0.2206 0.7956 0.8255 0.8103 0.6811 0.9665 0.9027 0.9524
0.0195 5.49 950 0.2213 0.7953 0.8259 0.8103 0.6811 0.9665 0.9029 0.9524
0.0285 5.55 960 0.2214 0.7955 0.8254 0.8102 0.6809 0.9665 0.9026 0.9524
0.0263 5.61 970 0.2213 0.7955 0.8254 0.8102 0.6809 0.9665 0.9026 0.9524
0.02 5.66 980 0.2214 0.7951 0.8258 0.8101 0.6809 0.9665 0.9028 0.9524
0.021 5.72 990 0.2214 0.7952 0.8261 0.8104 0.6812 0.9665 0.9030 0.9525
0.0233 5.78 1000 0.2214 0.7952 0.8261 0.8104 0.6812 0.9665 0.9030 0.9525

Framework versions

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