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PassbyGrocer
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hreb-resume

Token Classification
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Safetensors
bert
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@@ -21,11 +21,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2110
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- - Precision: 0.9394
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- - Recall: 0.9582
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- - F1: 0.9487
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- - Accuracy: 0.9817
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  ## Model description
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@@ -56,106 +56,106 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.0676 | 1.0 | 120 | 0.0879 | 0.8905 | 0.9464 | 0.9176 | 0.9751 |
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- | 0.0751 | 2.0 | 240 | 0.0774 | 0.9136 | 0.9518 | 0.9323 | 0.9796 |
61
- | 0.0342 | 3.0 | 360 | 0.0889 | 0.9198 | 0.9591 | 0.9390 | 0.9807 |
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- | 0.0261 | 4.0 | 480 | 0.1041 | 0.9317 | 0.9555 | 0.9434 | 0.9809 |
63
- | 0.0082 | 5.0 | 600 | 0.1255 | 0.9308 | 0.9536 | 0.9421 | 0.9802 |
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- | 0.0134 | 6.0 | 720 | 0.1389 | 0.9286 | 0.9573 | 0.9427 | 0.9793 |
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- | 0.0089 | 7.0 | 840 | 0.1369 | 0.9366 | 0.9536 | 0.9450 | 0.9798 |
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- | 0.0093 | 8.0 | 960 | 0.1272 | 0.9393 | 0.9564 | 0.9477 | 0.9815 |
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- | 0.0044 | 9.0 | 1080 | 0.1384 | 0.9332 | 0.9518 | 0.9424 | 0.9803 |
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- | 0.0017 | 10.0 | 1200 | 0.1662 | 0.9401 | 0.9564 | 0.9482 | 0.9788 |
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- | 0.001 | 11.0 | 1320 | 0.1569 | 0.9368 | 0.9564 | 0.9465 | 0.9801 |
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- | 0.0007 | 12.0 | 1440 | 0.1724 | 0.9384 | 0.9564 | 0.9473 | 0.9801 |
71
- | 0.0017 | 13.0 | 1560 | 0.1794 | 0.9291 | 0.9536 | 0.9412 | 0.9790 |
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- | 0.0004 | 14.0 | 1680 | 0.1716 | 0.9325 | 0.9545 | 0.9434 | 0.9795 |
73
- | 0.0027 | 15.0 | 1800 | 0.1679 | 0.9435 | 0.9573 | 0.9504 | 0.9807 |
74
- | 0.0048 | 16.0 | 1920 | 0.1753 | 0.9401 | 0.9564 | 0.9482 | 0.9800 |
75
- | 0.001 | 17.0 | 2040 | 0.1872 | 0.9359 | 0.9564 | 0.9460 | 0.9793 |
76
- | 0.0009 | 18.0 | 2160 | 0.1851 | 0.9453 | 0.9582 | 0.9517 | 0.9798 |
77
- | 0.0067 | 19.0 | 2280 | 0.1645 | 0.9461 | 0.9582 | 0.9521 | 0.9805 |
78
- | 0.0002 | 20.0 | 2400 | 0.1712 | 0.9352 | 0.9573 | 0.9461 | 0.9798 |
79
- | 0.0023 | 21.0 | 2520 | 0.1779 | 0.9412 | 0.96 | 0.9505 | 0.9805 |
80
- | 0.0001 | 22.0 | 2640 | 0.1804 | 0.9402 | 0.9582 | 0.9491 | 0.9806 |
81
- | 0.0002 | 23.0 | 2760 | 0.1868 | 0.9386 | 0.9591 | 0.9487 | 0.9808 |
82
- | 0.0017 | 24.0 | 2880 | 0.1742 | 0.9358 | 0.9545 | 0.9451 | 0.9811 |
83
- | 0.0006 | 25.0 | 3000 | 0.1792 | 0.9452 | 0.9573 | 0.9512 | 0.9818 |
84
- | 0.0011 | 26.0 | 3120 | 0.1801 | 0.9265 | 0.9518 | 0.9390 | 0.9778 |
85
- | 0.0002 | 27.0 | 3240 | 0.2164 | 0.9402 | 0.9582 | 0.9491 | 0.9783 |
86
- | 0.0001 | 28.0 | 3360 | 0.1851 | 0.9425 | 0.9545 | 0.9485 | 0.9821 |
87
- | 0.0001 | 29.0 | 3480 | 0.1889 | 0.9417 | 0.9545 | 0.9481 | 0.9813 |
88
- | 0.0005 | 30.0 | 3600 | 0.1914 | 0.9460 | 0.9564 | 0.9512 | 0.9804 |
89
- | 0.0023 | 31.0 | 3720 | 0.1854 | 0.9497 | 0.9609 | 0.9553 | 0.9823 |
90
- | 0.0004 | 32.0 | 3840 | 0.1883 | 0.9409 | 0.9545 | 0.9477 | 0.9807 |
91
- | 0.0005 | 33.0 | 3960 | 0.1789 | 0.9470 | 0.9591 | 0.9530 | 0.9819 |
92
- | 0.0002 | 34.0 | 4080 | 0.2061 | 0.9453 | 0.9582 | 0.9517 | 0.9802 |
93
- | 0.0001 | 35.0 | 4200 | 0.1968 | 0.9479 | 0.9591 | 0.9535 | 0.9812 |
94
- | 0.0001 | 36.0 | 4320 | 0.2021 | 0.9479 | 0.9591 | 0.9535 | 0.9813 |
95
- | 0.0002 | 37.0 | 4440 | 0.2001 | 0.9437 | 0.96 | 0.9518 | 0.9812 |
96
- | 0.0016 | 38.0 | 4560 | 0.1796 | 0.9340 | 0.9518 | 0.9428 | 0.9805 |
97
- | 0.0001 | 39.0 | 4680 | 0.1759 | 0.9461 | 0.9582 | 0.9521 | 0.9806 |
98
- | 0.0017 | 40.0 | 4800 | 0.1789 | 0.9462 | 0.96 | 0.9531 | 0.9816 |
99
- | 0.0053 | 41.0 | 4920 | 0.1794 | 0.9436 | 0.9591 | 0.9513 | 0.9816 |
100
- | 0.0 | 42.0 | 5040 | 0.1849 | 0.9418 | 0.9555 | 0.9486 | 0.9814 |
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- | 0.0 | 43.0 | 5160 | 0.1888 | 0.9453 | 0.9582 | 0.9517 | 0.9814 |
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- | 0.0001 | 44.0 | 5280 | 0.1972 | 0.9340 | 0.9527 | 0.9433 | 0.9788 |
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- | 0.0001 | 45.0 | 5400 | 0.1898 | 0.9400 | 0.9545 | 0.9472 | 0.9793 |
104
- | 0.001 | 46.0 | 5520 | 0.1926 | 0.9392 | 0.9545 | 0.9468 | 0.9798 |
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- | 0.0001 | 47.0 | 5640 | 0.1932 | 0.9383 | 0.9545 | 0.9464 | 0.9796 |
106
- | 0.0 | 48.0 | 5760 | 0.1978 | 0.9340 | 0.9518 | 0.9428 | 0.9796 |
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- | 0.0 | 49.0 | 5880 | 0.1978 | 0.9392 | 0.9545 | 0.9468 | 0.9801 |
108
- | 0.0003 | 50.0 | 6000 | 0.2032 | 0.9366 | 0.9536 | 0.9450 | 0.9798 |
109
- | 0.0 | 51.0 | 6120 | 0.2091 | 0.9367 | 0.9555 | 0.9460 | 0.9800 |
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- | 0.0 | 52.0 | 6240 | 0.2040 | 0.9427 | 0.9564 | 0.9495 | 0.9799 |
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- | 0.0001 | 53.0 | 6360 | 0.2029 | 0.9368 | 0.9564 | 0.9465 | 0.9810 |
112
- | 0.0001 | 54.0 | 6480 | 0.2217 | 0.9391 | 0.9527 | 0.9458 | 0.9796 |
113
- | 0.0002 | 55.0 | 6600 | 0.2129 | 0.9435 | 0.9573 | 0.9504 | 0.9800 |
114
- | 0.0 | 56.0 | 6720 | 0.2186 | 0.9443 | 0.9555 | 0.9498 | 0.9798 |
115
- | 0.0 | 57.0 | 6840 | 0.2201 | 0.9451 | 0.9555 | 0.9503 | 0.9800 |
116
- | 0.0 | 58.0 | 6960 | 0.1975 | 0.9393 | 0.9564 | 0.9477 | 0.9815 |
117
- | 0.0 | 59.0 | 7080 | 0.2077 | 0.9444 | 0.9573 | 0.9508 | 0.9810 |
118
- | 0.0001 | 60.0 | 7200 | 0.2111 | 0.9452 | 0.9564 | 0.9507 | 0.9801 |
119
- | 0.0 | 61.0 | 7320 | 0.2132 | 0.9480 | 0.9618 | 0.9549 | 0.9824 |
120
- | 0.0 | 62.0 | 7440 | 0.2058 | 0.9442 | 0.9545 | 0.9494 | 0.9808 |
121
- | 0.0023 | 63.0 | 7560 | 0.1970 | 0.9522 | 0.9591 | 0.9556 | 0.9812 |
122
- | 0.0 | 64.0 | 7680 | 0.2055 | 0.9497 | 0.9609 | 0.9553 | 0.9805 |
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- | 0.0 | 65.0 | 7800 | 0.2050 | 0.9488 | 0.96 | 0.9544 | 0.9808 |
124
- | 0.0 | 66.0 | 7920 | 0.2336 | 0.9399 | 0.9527 | 0.9463 | 0.9778 |
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- | 0.0 | 67.0 | 8040 | 0.1999 | 0.9447 | 0.9627 | 0.9536 | 0.9817 |
126
- | 0.0 | 68.0 | 8160 | 0.2082 | 0.9419 | 0.9582 | 0.9500 | 0.9806 |
127
- | 0.0 | 69.0 | 8280 | 0.2113 | 0.9419 | 0.9582 | 0.9500 | 0.9806 |
128
- | 0.0 | 70.0 | 8400 | 0.2164 | 0.9436 | 0.9591 | 0.9513 | 0.9803 |
129
- | 0.0 | 71.0 | 8520 | 0.2041 | 0.9411 | 0.9582 | 0.9495 | 0.9807 |
130
- | 0.0 | 72.0 | 8640 | 0.2099 | 0.9470 | 0.9582 | 0.9526 | 0.9798 |
131
- | 0.0008 | 73.0 | 8760 | 0.2107 | 0.9444 | 0.9573 | 0.9508 | 0.9798 |
132
- | 0.0 | 74.0 | 8880 | 0.2119 | 0.9470 | 0.9582 | 0.9526 | 0.9798 |
133
- | 0.0 | 75.0 | 9000 | 0.2079 | 0.9470 | 0.9582 | 0.9526 | 0.9798 |
134
- | 0.0 | 76.0 | 9120 | 0.2132 | 0.9419 | 0.9573 | 0.9495 | 0.9798 |
135
- | 0.0 | 77.0 | 9240 | 0.2163 | 0.9367 | 0.9545 | 0.9455 | 0.9797 |
136
- | 0.0 | 78.0 | 9360 | 0.2168 | 0.9384 | 0.9555 | 0.9468 | 0.9796 |
137
- | 0.0 | 79.0 | 9480 | 0.2125 | 0.9436 | 0.9582 | 0.9508 | 0.9808 |
138
- | 0.0 | 80.0 | 9600 | 0.2151 | 0.9375 | 0.9545 | 0.9459 | 0.9800 |
139
- | 0.0 | 81.0 | 9720 | 0.2150 | 0.9367 | 0.9545 | 0.9455 | 0.9799 |
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- | 0.0 | 82.0 | 9840 | 0.2157 | 0.9368 | 0.9564 | 0.9465 | 0.9803 |
141
- | 0.0 | 83.0 | 9960 | 0.2159 | 0.9368 | 0.9564 | 0.9465 | 0.9805 |
142
- | 0.0 | 84.0 | 10080 | 0.2160 | 0.9368 | 0.9564 | 0.9465 | 0.9801 |
143
- | 0.0 | 85.0 | 10200 | 0.2164 | 0.9367 | 0.9545 | 0.9455 | 0.9799 |
144
- | 0.0 | 86.0 | 10320 | 0.2175 | 0.9384 | 0.9555 | 0.9468 | 0.9800 |
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- | 0.0 | 87.0 | 10440 | 0.2190 | 0.9384 | 0.9555 | 0.9468 | 0.9801 |
146
- | 0.0002 | 88.0 | 10560 | 0.2193 | 0.9375 | 0.9545 | 0.9459 | 0.9801 |
147
- | 0.0 | 89.0 | 10680 | 0.2176 | 0.9383 | 0.9545 | 0.9464 | 0.9798 |
148
- | 0.0 | 90.0 | 10800 | 0.2205 | 0.9410 | 0.9573 | 0.9491 | 0.9803 |
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- | 0.0 | 91.0 | 10920 | 0.2094 | 0.9368 | 0.9564 | 0.9465 | 0.9805 |
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- | 0.0 | 92.0 | 11040 | 0.2094 | 0.9351 | 0.9555 | 0.9451 | 0.9804 |
151
- | 0.0 | 93.0 | 11160 | 0.2096 | 0.9376 | 0.9564 | 0.9469 | 0.9808 |
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- | 0.0 | 94.0 | 11280 | 0.2100 | 0.9376 | 0.9564 | 0.9469 | 0.9808 |
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- | 0.0 | 95.0 | 11400 | 0.2104 | 0.9401 | 0.9555 | 0.9477 | 0.9812 |
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- | 0.0 | 96.0 | 11520 | 0.2107 | 0.9410 | 0.9564 | 0.9486 | 0.9814 |
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- | 0.0 | 97.0 | 11640 | 0.2107 | 0.9401 | 0.9564 | 0.9482 | 0.9816 |
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- | 0.0 | 98.0 | 11760 | 0.2109 | 0.9402 | 0.9582 | 0.9491 | 0.9818 |
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- | 0.0 | 99.0 | 11880 | 0.2110 | 0.9394 | 0.9582 | 0.9487 | 0.9817 |
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- | 0.0 | 100.0 | 12000 | 0.2110 | 0.9394 | 0.9582 | 0.9487 | 0.9817 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2496
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+ - Precision: 0.9534
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+ - Recall: 0.9609
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+ - F1: 0.9572
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+ - Accuracy: 0.9756
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  ## Model description
31
 
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0932 | 1.0 | 134 | 0.0917 | 0.9224 | 0.9496 | 0.9358 | 0.9734 |
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+ | 0.0356 | 2.0 | 268 | 0.0776 | 0.9343 | 0.9635 | 0.9487 | 0.9777 |
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+ | 0.0363 | 3.0 | 402 | 0.1026 | 0.9309 | 0.9479 | 0.9393 | 0.9727 |
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+ | 0.0278 | 4.0 | 536 | 0.1041 | 0.9408 | 0.9531 | 0.9469 | 0.9750 |
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+ | 0.0131 | 5.0 | 670 | 0.1194 | 0.9411 | 0.9574 | 0.9492 | 0.9746 |
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+ | 0.0071 | 6.0 | 804 | 0.1491 | 0.9593 | 0.9618 | 0.9605 | 0.9768 |
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+ | 0.0042 | 7.0 | 938 | 0.1474 | 0.9567 | 0.9609 | 0.9588 | 0.9749 |
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+ | 0.0105 | 8.0 | 1072 | 0.1318 | 0.9527 | 0.9635 | 0.9581 | 0.9768 |
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+ | 0.0039 | 9.0 | 1206 | 0.1616 | 0.9374 | 0.9496 | 0.9435 | 0.9724 |
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+ | 0.0023 | 10.0 | 1340 | 0.1547 | 0.9452 | 0.9592 | 0.9521 | 0.9755 |
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+ | 0.0026 | 11.0 | 1474 | 0.1585 | 0.9447 | 0.9505 | 0.9476 | 0.9746 |
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+ | 0.0046 | 12.0 | 1608 | 0.1492 | 0.9411 | 0.9574 | 0.9492 | 0.9748 |
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+ | 0.0051 | 13.0 | 1742 | 0.1440 | 0.9519 | 0.9626 | 0.9572 | 0.9755 |
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+ | 0.0037 | 14.0 | 1876 | 0.1651 | 0.9516 | 0.9574 | 0.9545 | 0.9743 |
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+ | 0.0006 | 15.0 | 2010 | 0.1641 | 0.9517 | 0.9583 | 0.9550 | 0.9755 |
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+ | 0.0003 | 16.0 | 2144 | 0.1957 | 0.9536 | 0.9635 | 0.9585 | 0.9734 |
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+ | 0.0002 | 17.0 | 2278 | 0.1896 | 0.9469 | 0.9600 | 0.9534 | 0.9734 |
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+ | 0.0016 | 18.0 | 2412 | 0.1877 | 0.9419 | 0.9574 | 0.9496 | 0.9731 |
77
+ | 0.0001 | 19.0 | 2546 | 0.2117 | 0.9481 | 0.9531 | 0.9506 | 0.9722 |
78
+ | 0.0018 | 20.0 | 2680 | 0.2165 | 0.9540 | 0.9548 | 0.9544 | 0.9743 |
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+ | 0.0011 | 21.0 | 2814 | 0.2082 | 0.9534 | 0.9592 | 0.9563 | 0.9719 |
80
+ | 0.0006 | 22.0 | 2948 | 0.2052 | 0.9567 | 0.9592 | 0.9579 | 0.9730 |
81
+ | 0.0001 | 23.0 | 3082 | 0.2016 | 0.9551 | 0.9600 | 0.9575 | 0.9746 |
82
+ | 0.0019 | 24.0 | 3216 | 0.1969 | 0.9549 | 0.9557 | 0.9553 | 0.9746 |
83
+ | 0.001 | 25.0 | 3350 | 0.1927 | 0.9516 | 0.9557 | 0.9536 | 0.9742 |
84
+ | 0.0002 | 26.0 | 3484 | 0.1767 | 0.9601 | 0.9609 | 0.9605 | 0.9752 |
85
+ | 0.0003 | 27.0 | 3618 | 0.1936 | 0.9433 | 0.9540 | 0.9486 | 0.9735 |
86
+ | 0.0011 | 28.0 | 3752 | 0.1928 | 0.9425 | 0.9548 | 0.9486 | 0.9734 |
87
+ | 0.0001 | 29.0 | 3886 | 0.2076 | 0.9456 | 0.9522 | 0.9489 | 0.9739 |
88
+ | 0.0052 | 30.0 | 4020 | 0.2040 | 0.9418 | 0.9557 | 0.9487 | 0.9748 |
89
+ | 0.0013 | 31.0 | 4154 | 0.1716 | 0.9552 | 0.9635 | 0.9593 | 0.9749 |
90
+ | 0.0004 | 32.0 | 4288 | 0.1762 | 0.9475 | 0.9566 | 0.9520 | 0.9755 |
91
+ | 0.0004 | 33.0 | 4422 | 0.1479 | 0.9450 | 0.9557 | 0.9503 | 0.9768 |
92
+ | 0.0001 | 34.0 | 4556 | 0.1668 | 0.9469 | 0.9609 | 0.9539 | 0.9766 |
93
+ | 0.0019 | 35.0 | 4690 | 0.1884 | 0.9508 | 0.9574 | 0.9541 | 0.9766 |
94
+ | 0.0001 | 36.0 | 4824 | 0.1873 | 0.9451 | 0.9566 | 0.9508 | 0.9748 |
95
+ | 0.0012 | 37.0 | 4958 | 0.1807 | 0.9553 | 0.9652 | 0.9602 | 0.9763 |
96
+ | 0.0071 | 38.0 | 5092 | 0.1993 | 0.9558 | 0.9592 | 0.9575 | 0.9760 |
97
+ | 0.0 | 39.0 | 5226 | 0.1821 | 0.9483 | 0.9557 | 0.9520 | 0.9751 |
98
+ | 0.0008 | 40.0 | 5360 | 0.1968 | 0.9576 | 0.9626 | 0.9601 | 0.9757 |
99
+ | 0.0011 | 41.0 | 5494 | 0.1930 | 0.9493 | 0.9600 | 0.9546 | 0.9751 |
100
+ | 0.0016 | 42.0 | 5628 | 0.1974 | 0.9476 | 0.9592 | 0.9534 | 0.9753 |
101
+ | 0.0 | 43.0 | 5762 | 0.1935 | 0.9477 | 0.9600 | 0.9538 | 0.9759 |
102
+ | 0.0015 | 44.0 | 5896 | 0.1970 | 0.9525 | 0.9583 | 0.9554 | 0.9757 |
103
+ | 0.0008 | 45.0 | 6030 | 0.2079 | 0.9542 | 0.9592 | 0.9567 | 0.9775 |
104
+ | 0.0003 | 46.0 | 6164 | 0.1899 | 0.9435 | 0.9583 | 0.9509 | 0.9746 |
105
+ | 0.0047 | 47.0 | 6298 | 0.1822 | 0.9478 | 0.9626 | 0.9552 | 0.9748 |
106
+ | 0.0019 | 48.0 | 6432 | 0.1990 | 0.9543 | 0.9609 | 0.9576 | 0.9758 |
107
+ | 0.001 | 49.0 | 6566 | 0.2001 | 0.9518 | 0.9609 | 0.9563 | 0.9764 |
108
+ | 0.0 | 50.0 | 6700 | 0.1961 | 0.9493 | 0.9600 | 0.9546 | 0.9758 |
109
+ | 0.0 | 51.0 | 6834 | 0.2083 | 0.9551 | 0.9618 | 0.9584 | 0.9765 |
110
+ | 0.0 | 52.0 | 6968 | 0.2072 | 0.9558 | 0.9583 | 0.9570 | 0.9762 |
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+ | 0.0 | 53.0 | 7102 | 0.2112 | 0.9516 | 0.9574 | 0.9545 | 0.9764 |
112
+ | 0.0001 | 54.0 | 7236 | 0.2160 | 0.9560 | 0.9618 | 0.9589 | 0.9774 |
113
+ | 0.0 | 55.0 | 7370 | 0.2166 | 0.9560 | 0.9618 | 0.9589 | 0.9771 |
114
+ | 0.0 | 56.0 | 7504 | 0.2188 | 0.9560 | 0.9618 | 0.9589 | 0.9770 |
115
+ | 0.0 | 57.0 | 7638 | 0.2208 | 0.9568 | 0.9618 | 0.9593 | 0.9771 |
116
+ | 0.0007 | 58.0 | 7772 | 0.2301 | 0.9559 | 0.9600 | 0.9580 | 0.9764 |
117
+ | 0.0 | 59.0 | 7906 | 0.2317 | 0.9551 | 0.9600 | 0.9575 | 0.9764 |
118
+ | 0.0 | 60.0 | 8040 | 0.2326 | 0.9559 | 0.9600 | 0.9580 | 0.9759 |
119
+ | 0.0 | 61.0 | 8174 | 0.2349 | 0.9583 | 0.9592 | 0.9587 | 0.9761 |
120
+ | 0.0 | 62.0 | 8308 | 0.2378 | 0.9550 | 0.9592 | 0.9571 | 0.9768 |
121
+ | 0.0 | 63.0 | 8442 | 0.2406 | 0.9526 | 0.9600 | 0.9563 | 0.9767 |
122
+ | 0.0 | 64.0 | 8576 | 0.2367 | 0.9584 | 0.9600 | 0.9592 | 0.9771 |
123
+ | 0.0009 | 65.0 | 8710 | 0.2329 | 0.9584 | 0.9600 | 0.9592 | 0.9772 |
124
+ | 0.0 | 66.0 | 8844 | 0.2376 | 0.9524 | 0.9566 | 0.9545 | 0.9761 |
125
+ | 0.0009 | 67.0 | 8978 | 0.2381 | 0.9509 | 0.9583 | 0.9546 | 0.9759 |
126
+ | 0.0 | 68.0 | 9112 | 0.2236 | 0.9585 | 0.9644 | 0.9615 | 0.9778 |
127
+ | 0.0 | 69.0 | 9246 | 0.2194 | 0.9619 | 0.9644 | 0.9631 | 0.9769 |
128
+ | 0.0 | 70.0 | 9380 | 0.2192 | 0.9595 | 0.9670 | 0.9632 | 0.9775 |
129
+ | 0.0 | 71.0 | 9514 | 0.2124 | 0.9562 | 0.9679 | 0.9620 | 0.9777 |
130
+ | 0.0007 | 72.0 | 9648 | 0.2139 | 0.9587 | 0.9679 | 0.9633 | 0.9777 |
131
+ | 0.0 | 73.0 | 9782 | 0.2266 | 0.9552 | 0.9644 | 0.9598 | 0.9764 |
132
+ | 0.0 | 74.0 | 9916 | 0.2275 | 0.9593 | 0.9635 | 0.9614 | 0.9765 |
133
+ | 0.0 | 75.0 | 10050 | 0.2239 | 0.9577 | 0.9644 | 0.9610 | 0.9774 |
134
+ | 0.0 | 76.0 | 10184 | 0.2212 | 0.9578 | 0.9652 | 0.9615 | 0.9760 |
135
+ | 0.0 | 77.0 | 10318 | 0.2208 | 0.9560 | 0.9635 | 0.9598 | 0.9758 |
136
+ | 0.0 | 78.0 | 10452 | 0.2259 | 0.9577 | 0.9635 | 0.9606 | 0.9757 |
137
+ | 0.0 | 79.0 | 10586 | 0.2279 | 0.9593 | 0.9635 | 0.9614 | 0.9756 |
138
+ | 0.0 | 80.0 | 10720 | 0.2294 | 0.9577 | 0.9635 | 0.9606 | 0.9756 |
139
+ | 0.0 | 81.0 | 10854 | 0.2380 | 0.9492 | 0.9574 | 0.9533 | 0.9766 |
140
+ | 0.0 | 82.0 | 10988 | 0.2405 | 0.9577 | 0.9644 | 0.9610 | 0.9759 |
141
+ | 0.0 | 83.0 | 11122 | 0.2442 | 0.9511 | 0.9635 | 0.9573 | 0.9751 |
142
+ | 0.0003 | 84.0 | 11256 | 0.2371 | 0.9552 | 0.9626 | 0.9589 | 0.9749 |
143
+ | 0.0 | 85.0 | 11390 | 0.2499 | 0.9559 | 0.9609 | 0.9584 | 0.9750 |
144
+ | 0.0 | 86.0 | 11524 | 0.2516 | 0.9601 | 0.9626 | 0.9614 | 0.9752 |
145
+ | 0.0 | 87.0 | 11658 | 0.2519 | 0.9593 | 0.9626 | 0.9610 | 0.9752 |
146
+ | 0.0008 | 88.0 | 11792 | 0.2516 | 0.9576 | 0.9618 | 0.9597 | 0.9752 |
147
+ | 0.0007 | 89.0 | 11926 | 0.2517 | 0.9593 | 0.9626 | 0.9610 | 0.9753 |
148
+ | 0.0 | 90.0 | 12060 | 0.2477 | 0.9559 | 0.9609 | 0.9584 | 0.9754 |
149
+ | 0.0 | 91.0 | 12194 | 0.2477 | 0.9559 | 0.9609 | 0.9584 | 0.9754 |
150
+ | 0.0 | 92.0 | 12328 | 0.2479 | 0.9543 | 0.9609 | 0.9576 | 0.9755 |
151
+ | 0.0007 | 93.0 | 12462 | 0.2482 | 0.9543 | 0.9609 | 0.9576 | 0.9754 |
152
+ | 0.0 | 94.0 | 12596 | 0.2487 | 0.9534 | 0.9609 | 0.9572 | 0.9755 |
153
+ | 0.0 | 95.0 | 12730 | 0.2490 | 0.9534 | 0.9609 | 0.9572 | 0.9755 |
154
+ | 0.0 | 96.0 | 12864 | 0.2495 | 0.9534 | 0.9609 | 0.9572 | 0.9755 |
155
+ | 0.0 | 97.0 | 12998 | 0.2489 | 0.9534 | 0.9609 | 0.9572 | 0.9755 |
156
+ | 0.0 | 98.0 | 13132 | 0.2495 | 0.9534 | 0.9609 | 0.9572 | 0.9756 |
157
+ | 0.0006 | 99.0 | 13266 | 0.2496 | 0.9534 | 0.9609 | 0.9572 | 0.9756 |
158
+ | 0.0 | 100.0 | 13400 | 0.2496 | 0.9534 | 0.9609 | 0.9572 | 0.9756 |
159
 
160
 
161
  ### Framework versions
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