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bert-suicide-detection-hk-large-new

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bert
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@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [hon9kon9ize/bert-large-cantonese](https://huggingface.co/hon9kon9ize/bert-large-cantonese) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2903
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- - Accuracy: 0.9467
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  ## Model description
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@@ -50,87 +50,88 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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- | 0.6203 | 0.0613 | 20 | 0.4148 | 0.8267 |
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- | 0.3246 | 0.1227 | 40 | 0.8805 | 0.8 |
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- | 0.5453 | 0.1840 | 60 | 0.3735 | 0.8667 |
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- | 0.4513 | 0.2454 | 80 | 0.4391 | 0.8867 |
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- | 0.7729 | 0.3067 | 100 | 0.4407 | 0.82 |
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- | 0.5867 | 0.3681 | 120 | 0.4013 | 0.8467 |
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- | 0.4073 | 0.4294 | 140 | 0.5397 | 0.86 |
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- | 0.1883 | 0.4908 | 160 | 0.7620 | 0.8667 |
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- | 0.4166 | 0.5521 | 180 | 0.6517 | 0.8933 |
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- | 0.4672 | 0.6135 | 200 | 0.6163 | 0.88 |
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- | 0.6858 | 0.6748 | 220 | 0.3484 | 0.8667 |
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- | 0.335 | 0.7362 | 240 | 0.6031 | 0.8533 |
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- | 0.4525 | 0.7975 | 260 | 0.6941 | 0.82 |
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- | 0.2385 | 0.8589 | 280 | 0.5618 | 0.88 |
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- | 0.4256 | 0.9202 | 300 | 0.5899 | 0.88 |
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- | 0.4934 | 0.9816 | 320 | 0.3289 | 0.9 |
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- | 0.277 | 1.0429 | 340 | 0.5671 | 0.88 |
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- | 0.5097 | 1.1043 | 360 | 0.5247 | 0.88 |
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- | 0.105 | 1.1656 | 380 | 0.4810 | 0.9 |
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- | 0.3976 | 1.2270 | 400 | 0.4562 | 0.8933 |
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- | 0.3506 | 1.2883 | 420 | 0.3943 | 0.8867 |
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- | 0.2057 | 1.3497 | 440 | 0.4944 | 0.8933 |
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- | 0.2788 | 1.4110 | 460 | 0.4718 | 0.9 |
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- | 0.4049 | 1.4724 | 480 | 0.5067 | 0.88 |
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- | 0.415 | 1.5337 | 500 | 0.4395 | 0.9 |
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- | 0.3565 | 1.5951 | 520 | 0.3682 | 0.9 |
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- | 0.3111 | 1.6564 | 540 | 0.3298 | 0.9 |
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- | 0.4191 | 1.7178 | 560 | 0.4493 | 0.8733 |
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- | 0.2731 | 1.7791 | 580 | 0.3832 | 0.9067 |
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- | 0.1803 | 1.8405 | 600 | 0.4403 | 0.8933 |
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- | 0.4462 | 1.9018 | 620 | 0.3844 | 0.9067 |
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- | 0.0025 | 1.9632 | 640 | 0.4563 | 0.9067 |
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- | 0.1574 | 2.0245 | 660 | 0.5508 | 0.8933 |
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- | 0.0927 | 2.0859 | 680 | 0.5529 | 0.9067 |
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- | 0.184 | 2.1472 | 700 | 0.5161 | 0.9 |
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- | 0.2446 | 2.2086 | 720 | 0.5064 | 0.8933 |
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- | 0.2498 | 2.2699 | 740 | 0.4034 | 0.92 |
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- | 0.2217 | 2.3313 | 760 | 0.5095 | 0.8733 |
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- | 0.2938 | 2.3926 | 780 | 0.3754 | 0.9067 |
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- | 0.109 | 2.4540 | 800 | 0.4771 | 0.8933 |
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- | 0.0282 | 2.5153 | 820 | 0.5535 | 0.8933 |
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- | 0.2455 | 2.5767 | 840 | 0.4206 | 0.9067 |
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- | 0.4728 | 2.6380 | 860 | 0.3018 | 0.9067 |
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- | 0.1145 | 2.6994 | 880 | 0.3053 | 0.9067 |
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- | 0.1045 | 2.7607 | 900 | 0.3431 | 0.9067 |
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- | 0.2207 | 2.8221 | 920 | 0.6482 | 0.86 |
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- | 0.427 | 2.8834 | 940 | 0.4396 | 0.9133 |
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- | 0.1898 | 2.9448 | 960 | 0.3327 | 0.92 |
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- | 0.0019 | 3.0061 | 980 | 0.3993 | 0.92 |
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- | 0.0842 | 3.0675 | 1000 | 0.4166 | 0.9267 |
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- | 0.1619 | 3.1288 | 1020 | 0.4181 | 0.9133 |
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- | 0.1849 | 3.1902 | 1040 | 0.4727 | 0.92 |
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- | 0.1949 | 3.2515 | 1060 | 0.3346 | 0.8933 |
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- | 0.1796 | 3.3129 | 1080 | 0.3471 | 0.9267 |
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- | 0.086 | 3.3742 | 1100 | 0.4089 | 0.8867 |
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- | 0.0187 | 3.4356 | 1120 | 0.3868 | 0.92 |
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- | 0.0768 | 3.4969 | 1140 | 0.4095 | 0.9267 |
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- | 0.0008 | 3.5583 | 1160 | 0.3780 | 0.9067 |
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- | 0.183 | 3.6196 | 1180 | 0.3827 | 0.9 |
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- | 0.204 | 3.6810 | 1200 | 0.5133 | 0.9 |
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- | 0.0758 | 3.7423 | 1220 | 0.4280 | 0.9133 |
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- | 0.0237 | 3.8037 | 1240 | 0.3942 | 0.92 |
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- | 0.2143 | 3.8650 | 1260 | 0.3680 | 0.9067 |
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- | 0.0106 | 3.9264 | 1280 | 0.5633 | 0.8867 |
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- | 0.2221 | 3.9877 | 1300 | 0.3815 | 0.92 |
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- | 0.0212 | 4.0491 | 1320 | 0.4599 | 0.9267 |
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- | 0.1678 | 4.1104 | 1340 | 0.3458 | 0.92 |
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- | 0.1153 | 4.1718 | 1360 | 0.3261 | 0.92 |
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- | 0.0006 | 4.2331 | 1380 | 0.3404 | 0.9133 |
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- | 0.0193 | 4.2945 | 1400 | 0.3602 | 0.92 |
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- | 0.0994 | 4.3558 | 1420 | 0.3303 | 0.94 |
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- | 0.0032 | 4.4172 | 1440 | 0.2885 | 0.94 |
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- | 0.0008 | 4.4785 | 1460 | 0.3112 | 0.92 |
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- | 0.0823 | 4.5399 | 1480 | 0.3145 | 0.9267 |
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- | 0.0086 | 4.6012 | 1500 | 0.2954 | 0.94 |
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- | 0.0009 | 4.6626 | 1520 | 0.3082 | 0.94 |
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- | 0.1619 | 4.7239 | 1540 | 0.2928 | 0.94 |
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- | 0.0004 | 4.7853 | 1560 | 0.2909 | 0.9333 |
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- | 0.0006 | 4.8466 | 1580 | 0.2879 | 0.9467 |
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- | 0.0005 | 4.9080 | 1600 | 0.2894 | 0.9467 |
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- | 0.0559 | 4.9693 | 1620 | 0.2903 | 0.9467 |
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [hon9kon9ize/bert-large-cantonese](https://huggingface.co/hon9kon9ize/bert-large-cantonese) on the None dataset.
20
  It achieves the following results on the evaluation set:
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+ - Loss: 0.4945
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+ - Accuracy: 0.9085
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | 0.5426 | 0.0604 | 20 | 0.7869 | 0.7451 |
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+ | 0.5984 | 0.1208 | 40 | 0.3943 | 0.7908 |
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+ | 0.4864 | 0.1813 | 60 | 0.9365 | 0.7843 |
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+ | 0.6039 | 0.2417 | 80 | 0.6580 | 0.7712 |
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+ | 0.5741 | 0.3021 | 100 | 0.3454 | 0.8235 |
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+ | 0.4276 | 0.3625 | 120 | 0.5421 | 0.8170 |
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+ | 0.4342 | 0.4230 | 140 | 0.4258 | 0.8562 |
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+ | 0.4915 | 0.4834 | 160 | 0.5961 | 0.8301 |
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+ | 0.4127 | 0.5438 | 180 | 0.2987 | 0.8693 |
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+ | 0.3166 | 0.6042 | 200 | 0.3308 | 0.8693 |
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+ | 0.4018 | 0.6647 | 220 | 0.5286 | 0.8039 |
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+ | 0.3007 | 0.7251 | 240 | 0.5845 | 0.8627 |
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+ | 0.4893 | 0.7855 | 260 | 0.3662 | 0.8627 |
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+ | 0.274 | 0.8459 | 280 | 0.3483 | 0.8693 |
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+ | 0.5741 | 0.9063 | 300 | 0.3280 | 0.8824 |
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+ | 0.3752 | 0.9668 | 320 | 0.5251 | 0.8889 |
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+ | 0.2711 | 1.0272 | 340 | 0.6097 | 0.8562 |
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+ | 0.2369 | 1.0876 | 360 | 0.5457 | 0.8693 |
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+ | 0.3756 | 1.1480 | 380 | 0.6890 | 0.8758 |
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+ | 0.6575 | 1.2085 | 400 | 0.4709 | 0.8693 |
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+ | 0.3268 | 1.2689 | 420 | 0.5219 | 0.8497 |
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+ | 0.3994 | 1.3293 | 440 | 0.4282 | 0.8693 |
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+ | 0.0879 | 1.3897 | 460 | 0.6294 | 0.8758 |
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+ | 0.2566 | 1.4502 | 480 | 0.7143 | 0.8627 |
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+ | 0.2897 | 1.5106 | 500 | 0.6120 | 0.8693 |
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+ | 0.321 | 1.5710 | 520 | 0.4749 | 0.8758 |
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+ | 0.1871 | 1.6314 | 540 | 0.4392 | 0.9085 |
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+ | 0.1654 | 1.6918 | 560 | 0.4663 | 0.9085 |
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+ | 0.3166 | 1.7523 | 580 | 0.5048 | 0.8889 |
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+ | 0.222 | 1.8127 | 600 | 0.4550 | 0.9085 |
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+ | 0.4299 | 1.8731 | 620 | 0.3445 | 0.9085 |
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+ | 0.0942 | 1.9335 | 640 | 0.3735 | 0.9281 |
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+ | 0.3991 | 1.9940 | 660 | 0.3646 | 0.9085 |
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+ | 0.0581 | 2.0544 | 680 | 0.3527 | 0.9085 |
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+ | 0.2712 | 2.1148 | 700 | 0.4270 | 0.9020 |
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+ | 0.0443 | 2.1752 | 720 | 0.5462 | 0.8954 |
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+ | 0.3831 | 2.2356 | 740 | 0.3419 | 0.9216 |
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+ | 0.2267 | 2.2961 | 760 | 0.4925 | 0.8889 |
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+ | 0.1821 | 2.3565 | 780 | 0.3625 | 0.9216 |
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+ | 0.2926 | 2.4169 | 800 | 0.3671 | 0.9020 |
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+ | 0.2507 | 2.4773 | 820 | 0.3853 | 0.9020 |
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+ | 0.2446 | 2.5378 | 840 | 0.4571 | 0.8954 |
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+ | 0.1926 | 2.5982 | 860 | 0.5436 | 0.8497 |
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+ | 0.1725 | 2.6586 | 880 | 0.6576 | 0.8497 |
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+ | 0.2033 | 2.7190 | 900 | 0.4772 | 0.9020 |
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+ | 0.0095 | 2.7795 | 920 | 0.4103 | 0.9150 |
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+ | 0.2896 | 2.8399 | 940 | 0.4333 | 0.9085 |
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+ | 0.2661 | 2.9003 | 960 | 0.5793 | 0.8889 |
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+ | 0.1338 | 2.9607 | 980 | 0.4543 | 0.8954 |
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+ | 0.0751 | 3.0211 | 1000 | 0.5029 | 0.8954 |
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+ | 0.2093 | 3.0816 | 1020 | 0.4631 | 0.9020 |
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+ | 0.2436 | 3.1420 | 1040 | 0.5888 | 0.8693 |
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+ | 0.1375 | 3.2024 | 1060 | 0.6457 | 0.8889 |
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+ | 0.0049 | 3.2628 | 1080 | 0.6601 | 0.8889 |
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+ | 0.0089 | 3.3233 | 1100 | 0.6462 | 0.8824 |
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+ | 0.0616 | 3.3837 | 1120 | 0.6607 | 0.8889 |
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+ | 0.006 | 3.4441 | 1140 | 0.6243 | 0.9020 |
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+ | 0.1769 | 3.5045 | 1160 | 0.5257 | 0.9020 |
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+ | 0.0044 | 3.5650 | 1180 | 0.5508 | 0.9085 |
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+ | 0.2295 | 3.6254 | 1200 | 0.4846 | 0.9150 |
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+ | 0.1175 | 3.6858 | 1220 | 0.4764 | 0.9020 |
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+ | 0.0746 | 3.7462 | 1240 | 0.4761 | 0.9020 |
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+ | 0.0222 | 3.8066 | 1260 | 0.4836 | 0.9020 |
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+ | 0.0012 | 3.8671 | 1280 | 0.4775 | 0.9216 |
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+ | 0.2131 | 3.9275 | 1300 | 0.4607 | 0.9020 |
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+ | 0.0006 | 3.9879 | 1320 | 0.4935 | 0.9085 |
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+ | 0.0758 | 4.0483 | 1340 | 0.4592 | 0.9020 |
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+ | 0.1466 | 4.1088 | 1360 | 0.4464 | 0.9085 |
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+ | 0.0488 | 4.1692 | 1380 | 0.4816 | 0.9085 |
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+ | 0.0014 | 4.2296 | 1400 | 0.4570 | 0.9150 |
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+ | 0.082 | 4.2900 | 1420 | 0.4545 | 0.9216 |
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+ | 0.0009 | 4.3505 | 1440 | 0.4721 | 0.9150 |
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+ | 0.0008 | 4.4109 | 1460 | 0.4874 | 0.9216 |
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+ | 0.0014 | 4.4713 | 1480 | 0.5003 | 0.9150 |
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+ | 0.1612 | 4.5317 | 1500 | 0.5064 | 0.9150 |
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+ | 0.2079 | 4.5921 | 1520 | 0.4994 | 0.9150 |
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+ | 0.1423 | 4.6526 | 1540 | 0.4835 | 0.9150 |
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+ | 0.0009 | 4.7130 | 1560 | 0.4825 | 0.9085 |
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+ | 0.0017 | 4.7734 | 1580 | 0.4918 | 0.9085 |
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+ | 0.0648 | 4.8338 | 1600 | 0.4917 | 0.9150 |
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+ | 0.0531 | 4.8943 | 1620 | 0.4919 | 0.9085 |
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+ | 0.0008 | 4.9547 | 1640 | 0.4945 | 0.9085 |
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  ### Framework versions