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sembr2023-bert-small

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

sembr2023-bert-small

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

  • Loss: 0.2324
  • Precision: 0.7915
  • Recall: 0.8418
  • F1: 0.8159
  • Iou: 0.6890
  • Accuracy: 0.9651
  • Balanced Accuracy: 0.9097
  • Overall Accuracy: 0.9481

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
0.4134 0.06 10 0.4107 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.371 0.12 20 0.3698 0 0.0 0.0 0.0 0.9080 0.5 0.9080
0.2913 0.18 30 0.2672 0.8443 0.4167 0.5580 0.3870 0.9393 0.7045 0.9283
0.2315 0.24 40 0.2184 0.8043 0.6761 0.7346 0.5806 0.9551 0.8297 0.9364
0.1693 0.3 50 0.2021 0.8064 0.7375 0.7704 0.6265 0.9596 0.8598 0.9396
0.1812 0.36 60 0.1869 0.8727 0.6847 0.7674 0.6225 0.9618 0.8373 0.9437
0.1745 0.42 70 0.1855 0.8021 0.7744 0.7880 0.6502 0.9617 0.8775 0.9421
0.1577 0.48 80 0.1817 0.8207 0.7641 0.7914 0.6548 0.9630 0.8736 0.9431
0.1458 0.55 90 0.1763 0.8183 0.7869 0.8023 0.6698 0.9643 0.8846 0.9449
0.1343 0.61 100 0.1772 0.8721 0.7372 0.7990 0.6652 0.9659 0.8631 0.9477
0.1442 0.67 110 0.1647 0.8388 0.7795 0.8081 0.6779 0.9659 0.8822 0.9483
0.1104 0.73 120 0.1678 0.8488 0.7679 0.8063 0.6755 0.9661 0.8770 0.9479
0.1089 0.79 130 0.1745 0.7882 0.8262 0.8068 0.6761 0.9636 0.9019 0.9434
0.1437 0.85 140 0.1768 0.7970 0.8206 0.8086 0.6787 0.9643 0.8997 0.9440
0.1104 0.91 150 0.1710 0.7961 0.8275 0.8115 0.6828 0.9646 0.9030 0.9446
0.0941 0.97 160 0.1647 0.8007 0.8167 0.8086 0.6787 0.9644 0.8980 0.9456
0.1146 1.03 170 0.1744 0.8026 0.8250 0.8136 0.6858 0.9652 0.9022 0.9456
0.0982 1.09 180 0.1636 0.8175 0.8191 0.8183 0.6925 0.9666 0.9003 0.9468
0.0875 1.15 190 0.1653 0.8305 0.8064 0.8183 0.6924 0.9671 0.8948 0.9476
0.0962 1.21 200 0.1610 0.8340 0.8076 0.8206 0.6958 0.9675 0.8957 0.9490
0.084 1.27 210 0.1671 0.8232 0.8177 0.8204 0.6955 0.9671 0.9000 0.9476
0.07 1.33 220 0.1665 0.7909 0.8545 0.8215 0.6971 0.9658 0.9158 0.9454
0.0785 1.39 230 0.1612 0.8411 0.8004 0.8202 0.6953 0.9677 0.8925 0.9496
0.0712 1.45 240 0.1638 0.8251 0.8161 0.8205 0.6957 0.9672 0.8993 0.9491
0.0683 1.52 250 0.1823 0.8097 0.8262 0.8179 0.6919 0.9662 0.9033 0.9463
0.0694 1.58 260 0.1717 0.8028 0.8408 0.8214 0.6969 0.9664 0.9099 0.9474
0.0809 1.64 270 0.1681 0.8304 0.8102 0.8202 0.6952 0.9673 0.8967 0.9491
0.0586 1.7 280 0.1811 0.8096 0.8391 0.8241 0.7008 0.9671 0.9096 0.9478
0.069 1.76 290 0.1855 0.8088 0.8284 0.8185 0.6928 0.9662 0.9043 0.9478
0.0739 1.82 300 0.1876 0.8148 0.8209 0.8178 0.6918 0.9664 0.9010 0.9476
0.0691 1.88 310 0.1741 0.8173 0.8206 0.8190 0.6934 0.9666 0.9010 0.9485
0.0728 1.94 320 0.1765 0.7941 0.8346 0.8139 0.6861 0.9649 0.9064 0.9469
0.0585 2.0 330 0.1800 0.8118 0.8166 0.8142 0.6866 0.9657 0.8987 0.9483
0.0602 2.06 340 0.1930 0.7969 0.8366 0.8162 0.6895 0.9654 0.9075 0.9461
0.0557 2.12 350 0.1832 0.7915 0.8401 0.8151 0.6879 0.9649 0.9089 0.9472
0.0491 2.18 360 0.1914 0.8131 0.8136 0.8134 0.6854 0.9657 0.8973 0.9489
0.0413 2.24 370 0.2116 0.7989 0.8288 0.8136 0.6857 0.9651 0.9038 0.9463
0.051 2.3 380 0.2073 0.7864 0.8454 0.8148 0.6875 0.9647 0.9111 0.9460
0.0529 2.36 390 0.1923 0.8103 0.8278 0.8190 0.6934 0.9663 0.9041 0.9496
0.0469 2.42 400 0.1808 0.8131 0.8217 0.8173 0.6911 0.9662 0.9013 0.9497
0.0579 2.48 410 0.2053 0.7795 0.8493 0.8129 0.6848 0.9640 0.9125 0.9464
0.0494 2.55 420 0.1953 0.7872 0.8457 0.8154 0.6883 0.9648 0.9113 0.9471
0.0468 2.61 430 0.1972 0.8064 0.8182 0.8123 0.6839 0.9652 0.8992 0.9488
0.0545 2.67 440 0.2116 0.7774 0.8455 0.8100 0.6807 0.9635 0.9105 0.9458
0.0544 2.73 450 0.1954 0.7868 0.8455 0.8151 0.6879 0.9647 0.9111 0.9472
0.044 2.79 460 0.2046 0.8149 0.8203 0.8175 0.6914 0.9663 0.9007 0.9491
0.0468 2.85 470 0.2036 0.8031 0.8321 0.8174 0.6911 0.9658 0.9057 0.9483
0.0457 2.91 480 0.1998 0.7923 0.8377 0.8144 0.6869 0.9649 0.9077 0.9479
0.0435 2.97 490 0.2077 0.7864 0.8432 0.8138 0.6860 0.9645 0.9100 0.9475
0.0489 3.03 500 0.2067 0.7933 0.8339 0.8131 0.6850 0.9647 0.9059 0.9478
0.0472 3.09 510 0.2204 0.7883 0.8464 0.8163 0.6896 0.9650 0.9117 0.9475
0.0469 3.15 520 0.2209 0.7821 0.8470 0.8132 0.6853 0.9642 0.9115 0.9467
0.0384 3.21 530 0.2147 0.7923 0.8367 0.8139 0.6862 0.9648 0.9072 0.9479
0.0322 3.27 540 0.2215 0.7842 0.8489 0.8153 0.6881 0.9646 0.9126 0.9475
0.0429 3.33 550 0.2184 0.7743 0.8504 0.8106 0.6815 0.9634 0.9127 0.9463
0.0348 3.39 560 0.2293 0.7642 0.8594 0.8090 0.6792 0.9627 0.9163 0.9451
0.0365 3.45 570 0.2221 0.7922 0.8411 0.8159 0.6891 0.9651 0.9094 0.9477
0.0374 3.52 580 0.2175 0.7917 0.8382 0.8143 0.6868 0.9648 0.9079 0.9479
0.0413 3.58 590 0.2111 0.8122 0.8243 0.8182 0.6924 0.9663 0.9025 0.9499
0.0362 3.64 600 0.2183 0.7883 0.8404 0.8135 0.6856 0.9646 0.9088 0.9479
0.0352 3.7 610 0.2124 0.8005 0.8340 0.8169 0.6905 0.9656 0.9065 0.9487
0.0301 3.76 620 0.2145 0.7993 0.8369 0.8177 0.6916 0.9657 0.9078 0.9488
0.0399 3.82 630 0.2188 0.8018 0.8318 0.8166 0.6900 0.9656 0.9055 0.9485
0.0366 3.88 640 0.2211 0.7969 0.8346 0.8153 0.6882 0.9652 0.9066 0.9478
0.0289 3.94 650 0.2201 0.7850 0.8468 0.8147 0.6874 0.9646 0.9116 0.9475
0.0367 4.0 660 0.2280 0.7859 0.8437 0.8138 0.6860 0.9645 0.9102 0.9475
0.0362 4.06 670 0.2226 0.7785 0.8502 0.8128 0.6846 0.9640 0.9128 0.9469
0.0376 4.12 680 0.2213 0.8006 0.8317 0.8159 0.6890 0.9655 0.9054 0.9490
0.0294 4.18 690 0.2212 0.8052 0.8271 0.8160 0.6892 0.9657 0.9034 0.9492
0.0318 4.24 700 0.2254 0.7874 0.8420 0.8138 0.6860 0.9646 0.9095 0.9477
0.0359 4.3 710 0.2250 0.7899 0.8432 0.8157 0.6887 0.9649 0.9102 0.9479
0.034 4.36 720 0.2264 0.7985 0.8380 0.8178 0.6917 0.9656 0.9083 0.9489
0.0334 4.42 730 0.2308 0.7871 0.8436 0.8144 0.6869 0.9646 0.9102 0.9475
0.036 4.48 740 0.2250 0.7936 0.8404 0.8163 0.6896 0.9652 0.9091 0.9485
0.0257 4.55 750 0.2267 0.7861 0.8456 0.8148 0.6874 0.9646 0.9112 0.9479
0.0354 4.61 760 0.2288 0.7943 0.8401 0.8166 0.6900 0.9653 0.9090 0.9484
0.0373 4.67 770 0.2320 0.7828 0.8471 0.8137 0.6859 0.9643 0.9117 0.9470
0.0272 4.73 780 0.2250 0.7994 0.8354 0.8170 0.6906 0.9656 0.9071 0.9487
0.034 4.79 790 0.2339 0.7861 0.8450 0.8145 0.6870 0.9646 0.9108 0.9473
0.0294 4.85 800 0.2262 0.7972 0.8381 0.8171 0.6908 0.9655 0.9082 0.9486
0.0353 4.91 810 0.2337 0.7833 0.8473 0.8140 0.6864 0.9644 0.9118 0.9472
0.0337 4.97 820 0.2273 0.7973 0.8372 0.8168 0.6903 0.9655 0.9078 0.9485
0.0309 5.03 830 0.2318 0.7917 0.8413 0.8157 0.6888 0.9650 0.9094 0.9481
0.026 5.09 840 0.2327 0.7932 0.8418 0.8168 0.6903 0.9653 0.9098 0.9483
0.0271 5.15 850 0.2317 0.7887 0.8459 0.8163 0.6896 0.9650 0.9115 0.9479
0.0352 5.21 860 0.2344 0.7914 0.8427 0.8162 0.6895 0.9651 0.9101 0.9481
0.0268 5.27 870 0.2306 0.7931 0.8417 0.8166 0.6901 0.9652 0.9097 0.9484
0.0248 5.33 880 0.2309 0.7889 0.8438 0.8155 0.6884 0.9649 0.9105 0.9480
0.0331 5.39 890 0.2306 0.7895 0.8432 0.8154 0.6884 0.9649 0.9102 0.9480
0.0265 5.45 900 0.2322 0.7944 0.8401 0.8166 0.6901 0.9653 0.9091 0.9484
0.0352 5.52 910 0.2326 0.7922 0.8419 0.8163 0.6896 0.9651 0.9098 0.9482
0.0368 5.58 920 0.2313 0.7911 0.8424 0.8160 0.6891 0.9651 0.9099 0.9481
0.0315 5.64 930 0.2313 0.7917 0.8420 0.8161 0.6893 0.9651 0.9098 0.9482
0.0251 5.7 940 0.2324 0.7919 0.8409 0.8156 0.6887 0.9650 0.9093 0.9481
0.0331 5.76 950 0.2327 0.7913 0.8414 0.8156 0.6886 0.9650 0.9095 0.9481
0.0361 5.82 960 0.2327 0.7904 0.8423 0.8155 0.6885 0.9650 0.9098 0.9480
0.0362 5.88 970 0.2325 0.7909 0.8419 0.8156 0.6887 0.9650 0.9097 0.9481
0.031 5.94 980 0.2324 0.7914 0.8418 0.8158 0.6889 0.9650 0.9097 0.9481
0.0316 6.0 990 0.2324 0.7915 0.8418 0.8159 0.6890 0.9651 0.9097 0.9481
0.0232 6.06 1000 0.2324 0.7915 0.8418 0.8159 0.6890 0.9651 0.9097 0.9481

Framework versions

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