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sembr2023-distilbert-base-multilingual-cased

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

sembr2023-distilbert-base-multilingual-cased

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

  • Loss: 0.2282
  • Precision: 0.7986
  • Recall: 0.8244
  • F1: 0.8113
  • Iou: 0.6825
  • Accuracy: 0.9666
  • Balanced Accuracy: 0.9023
  • Overall Accuracy: 0.9521

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
0.4021 0.06 10 0.3642 0 0.0 0.0 0.0 0.9130 0.5 0.9130
0.2804 0.12 20 0.2550 0.7462 0.5589 0.6391 0.4696 0.9451 0.7704 0.9331
0.2173 0.18 30 0.2038 0.9157 0.4420 0.5962 0.4247 0.9479 0.7191 0.9406
0.1831 0.24 40 0.1824 0.8317 0.7186 0.7710 0.6274 0.9629 0.8524 0.9455
0.1816 0.29 50 0.1829 0.7540 0.7857 0.7695 0.6254 0.9591 0.8807 0.9405
0.1461 0.35 60 0.1619 0.8520 0.7210 0.7810 0.6407 0.9648 0.8545 0.9498
0.1424 0.41 70 0.1568 0.8091 0.7717 0.7900 0.6528 0.9643 0.8772 0.9483
0.11 0.47 80 0.1550 0.8189 0.7760 0.7969 0.6624 0.9656 0.8798 0.9504
0.1373 0.53 90 0.1584 0.8248 0.7640 0.7933 0.6573 0.9654 0.8743 0.9484
0.1202 0.59 100 0.1612 0.8292 0.7608 0.7935 0.6577 0.9656 0.8729 0.9496
0.0966 0.65 110 0.1390 0.8575 0.7597 0.8057 0.6746 0.9681 0.8738 0.9551
0.0832 0.71 120 0.1569 0.8292 0.7858 0.8069 0.6763 0.9673 0.8852 0.9508
0.0914 0.76 130 0.1399 0.8295 0.8009 0.8150 0.6877 0.9684 0.8926 0.9529
0.0793 0.82 140 0.1456 0.8161 0.7914 0.8035 0.6716 0.9663 0.8872 0.9512
0.0903 0.88 150 0.1461 0.8218 0.7882 0.8047 0.6732 0.9667 0.8860 0.9523
0.0976 0.94 160 0.1380 0.7999 0.8304 0.8148 0.6875 0.9672 0.9053 0.9533
0.0776 1.0 170 0.1633 0.8053 0.8144 0.8099 0.6805 0.9667 0.8978 0.9492
0.0722 1.06 180 0.1581 0.8569 0.7739 0.8133 0.6853 0.9691 0.8808 0.9528
0.0787 1.12 190 0.1563 0.7780 0.8350 0.8055 0.6743 0.9649 0.9062 0.9495
0.0642 1.18 200 0.1483 0.8007 0.8115 0.8060 0.6751 0.9660 0.8961 0.9527
0.0582 1.24 210 0.1559 0.8244 0.8067 0.8154 0.6884 0.9682 0.8952 0.9534
0.0617 1.29 220 0.1359 0.8158 0.8115 0.8136 0.6858 0.9677 0.8970 0.9560
0.0508 1.35 230 0.1638 0.8372 0.7872 0.8114 0.6827 0.9682 0.8863 0.9548
0.049 1.41 240 0.1699 0.8585 0.7432 0.7967 0.6620 0.9670 0.8657 0.9523
0.0604 1.47 250 0.1452 0.7935 0.8374 0.8148 0.6875 0.9669 0.9083 0.9523
0.0512 1.53 260 0.1551 0.8353 0.7820 0.8078 0.6776 0.9676 0.8837 0.9542
0.0496 1.59 270 0.1686 0.7757 0.8490 0.8107 0.6816 0.9655 0.9128 0.9496
0.0486 1.65 280 0.1474 0.8464 0.7834 0.8137 0.6859 0.9688 0.8849 0.9557
0.0529 1.71 290 0.1470 0.8114 0.8136 0.8125 0.6842 0.9673 0.8978 0.9535
0.0585 1.76 300 0.1625 0.8193 0.8057 0.8124 0.6841 0.9676 0.8944 0.9527
0.0373 1.82 310 0.1414 0.8038 0.8240 0.8138 0.6860 0.9672 0.9024 0.9555
0.0474 1.88 320 0.1600 0.8117 0.8073 0.8095 0.6800 0.9670 0.8947 0.9541
0.0475 1.94 330 0.1825 0.7709 0.8418 0.8048 0.6733 0.9645 0.9090 0.9498
0.0596 2.0 340 0.1688 0.8185 0.8078 0.8131 0.6851 0.9677 0.8954 0.9533
0.0391 2.06 350 0.1775 0.8006 0.8142 0.8073 0.6769 0.9662 0.8974 0.9514
0.0285 2.12 360 0.1653 0.8047 0.8165 0.8105 0.6814 0.9668 0.8988 0.9536
0.0288 2.18 370 0.1855 0.7697 0.8376 0.8022 0.6698 0.9641 0.9069 0.9509
0.0244 2.24 380 0.1813 0.8053 0.8171 0.8112 0.6823 0.9669 0.8991 0.9530
0.0324 2.29 390 0.1663 0.8029 0.8252 0.8139 0.6862 0.9672 0.9030 0.9538
0.0306 2.35 400 0.1692 0.7949 0.8291 0.8117 0.6830 0.9665 0.9044 0.9529
0.0279 2.41 410 0.1812 0.8171 0.8056 0.8113 0.6825 0.9674 0.8942 0.9526
0.0287 2.47 420 0.1768 0.8196 0.8106 0.8151 0.6879 0.9680 0.8968 0.9540
0.0287 2.53 430 0.1849 0.7927 0.8271 0.8095 0.6800 0.9662 0.9032 0.9532
0.0328 2.59 440 0.1765 0.8031 0.8197 0.8113 0.6825 0.9668 0.9003 0.9536
0.0226 2.65 450 0.1928 0.7879 0.8403 0.8133 0.6853 0.9664 0.9094 0.9515
0.0308 2.71 460 0.1905 0.7858 0.8354 0.8098 0.6804 0.9659 0.9069 0.9515
0.0277 2.76 470 0.1890 0.8083 0.8168 0.8125 0.6842 0.9672 0.8992 0.9537
0.0229 2.82 480 0.1802 0.8125 0.8177 0.8151 0.6879 0.9677 0.8999 0.9540
0.0218 2.88 490 0.1895 0.7805 0.8370 0.8078 0.6775 0.9654 0.9073 0.9506
0.0283 2.94 500 0.1926 0.7854 0.8337 0.8088 0.6790 0.9657 0.9060 0.9516
0.0231 3.0 510 0.2023 0.7766 0.8432 0.8085 0.6786 0.9653 0.9101 0.9509
0.0172 3.06 520 0.2051 0.7811 0.8305 0.8051 0.6737 0.9650 0.9042 0.9509
0.0206 3.12 530 0.1918 0.7894 0.8339 0.8110 0.6822 0.9662 0.9064 0.9523
0.0243 3.18 540 0.1982 0.7992 0.8198 0.8094 0.6798 0.9664 0.9001 0.9526
0.0193 3.24 550 0.2036 0.8024 0.8165 0.8094 0.6798 0.9666 0.8987 0.9517
0.0212 3.29 560 0.1967 0.8093 0.8175 0.8134 0.6854 0.9674 0.8996 0.9535
0.0188 3.35 570 0.1944 0.8056 0.8188 0.8122 0.6837 0.9671 0.9000 0.9527
0.0176 3.41 580 0.1975 0.8014 0.8239 0.8125 0.6842 0.9669 0.9022 0.9528
0.0197 3.47 590 0.2118 0.8058 0.8186 0.8122 0.6837 0.9671 0.8999 0.9529
0.0142 3.53 600 0.2000 0.8107 0.8187 0.8147 0.6873 0.9676 0.9003 0.9540
0.0145 3.59 610 0.2095 0.7950 0.8278 0.8111 0.6822 0.9665 0.9037 0.9522
0.023 3.65 620 0.2107 0.7881 0.8268 0.8070 0.6764 0.9656 0.9028 0.9511
0.0156 3.71 630 0.2191 0.7814 0.8366 0.8081 0.6780 0.9654 0.9072 0.9507
0.0175 3.76 640 0.2090 0.8081 0.8202 0.8141 0.6865 0.9674 0.9008 0.9528
0.02 3.82 650 0.2160 0.8068 0.8224 0.8145 0.6871 0.9674 0.9018 0.9524
0.0148 3.88 660 0.2049 0.7972 0.8306 0.8136 0.6857 0.9669 0.9052 0.9526
0.0184 3.94 670 0.2122 0.7908 0.8342 0.8119 0.6834 0.9664 0.9066 0.9513
0.0178 4.0 680 0.2090 0.7925 0.8271 0.8094 0.6799 0.9661 0.9032 0.9519
0.0159 4.06 690 0.2137 0.7963 0.8293 0.8125 0.6842 0.9667 0.9045 0.9528
0.0172 4.12 700 0.2150 0.7917 0.8317 0.8112 0.6824 0.9663 0.9054 0.9520
0.0163 4.18 710 0.2174 0.8010 0.8250 0.8128 0.6846 0.9670 0.9027 0.9524
0.0117 4.24 720 0.2175 0.8036 0.8229 0.8131 0.6851 0.9671 0.9019 0.9530
0.0149 4.29 730 0.2225 0.7990 0.8242 0.8114 0.6826 0.9667 0.9022 0.9515
0.014 4.35 740 0.2157 0.7916 0.8273 0.8090 0.6793 0.9660 0.9033 0.9520
0.0139 4.41 750 0.2217 0.7952 0.8231 0.8089 0.6792 0.9662 0.9015 0.9512
0.0143 4.47 760 0.2201 0.7914 0.8317 0.8111 0.6822 0.9663 0.9054 0.9518
0.0147 4.53 770 0.2218 0.7945 0.8264 0.8101 0.6809 0.9663 0.9030 0.9517
0.0124 4.59 780 0.2300 0.7915 0.8337 0.8120 0.6835 0.9664 0.9064 0.9515
0.0157 4.65 790 0.2244 0.8038 0.8191 0.8114 0.6826 0.9669 0.9000 0.9520
0.0126 4.71 800 0.2324 0.7983 0.8256 0.8117 0.6831 0.9667 0.9029 0.9514
0.0137 4.76 810 0.2263 0.8000 0.8226 0.8112 0.6823 0.9667 0.9015 0.9520
0.0133 4.82 820 0.2295 0.7932 0.8299 0.8111 0.6823 0.9664 0.9046 0.9512
0.0155 4.88 830 0.2266 0.8019 0.8226 0.8121 0.6837 0.9669 0.9016 0.9520
0.0135 4.94 840 0.2286 0.7932 0.8318 0.8121 0.6836 0.9665 0.9056 0.9518
0.0073 5.0 850 0.2283 0.8019 0.8235 0.8125 0.6843 0.9670 0.9021 0.9520
0.0147 5.06 860 0.2292 0.7965 0.8290 0.8124 0.6841 0.9667 0.9044 0.9518
0.0139 5.12 870 0.2293 0.8013 0.8251 0.8130 0.6850 0.9670 0.9028 0.9520
0.0162 5.18 880 0.2277 0.7977 0.8261 0.8116 0.6830 0.9667 0.9031 0.9518
0.0111 5.24 890 0.2285 0.7941 0.8288 0.8111 0.6822 0.9664 0.9042 0.9517
0.0128 5.29 900 0.2273 0.7987 0.8240 0.8112 0.6823 0.9666 0.9021 0.9521
0.0129 5.35 910 0.2282 0.7989 0.8244 0.8114 0.6827 0.9667 0.9023 0.9520
0.0115 5.41 920 0.2285 0.7991 0.8246 0.8117 0.6830 0.9667 0.9024 0.9520
0.011 5.47 930 0.2283 0.7999 0.8242 0.8119 0.6833 0.9668 0.9023 0.9522
0.013 5.53 940 0.2281 0.7995 0.8239 0.8115 0.6828 0.9667 0.9021 0.9522
0.0132 5.59 950 0.2281 0.7993 0.8239 0.8114 0.6826 0.9667 0.9021 0.9522
0.0104 5.65 960 0.2281 0.7992 0.8244 0.8116 0.6829 0.9667 0.9023 0.9522
0.0143 5.71 970 0.2279 0.7988 0.8245 0.8114 0.6827 0.9667 0.9024 0.9521
0.0133 5.76 980 0.2280 0.7988 0.8241 0.8113 0.6825 0.9667 0.9022 0.9521
0.0079 5.82 990 0.2282 0.7986 0.8244 0.8113 0.6825 0.9666 0.9023 0.9521
0.0108 5.88 1000 0.2282 0.7986 0.8244 0.8113 0.6825 0.9666 0.9023 0.9521

Framework versions

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