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Token Classification
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Safetensors
bert
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@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0820
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- - Precision: 0.9583
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- - Recall: 0.9664
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- - F1: 0.9623
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- - Accuracy: 0.9953
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  ## Model description
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@@ -55,106 +55,106 @@ 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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- | No log | 1.0 | 4 | 0.7480 | 0.0 | 0.0 | 0.0 | 0.8800 |
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- | No log | 2.0 | 8 | 0.5628 | 0.0 | 0.0 | 0.0 | 0.8800 |
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- | No log | 3.0 | 12 | 0.4862 | 0.0 | 0.0 | 0.0 | 0.8800 |
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- | No log | 4.0 | 16 | 0.4488 | 0.0 | 0.0 | 0.0 | 0.8800 |
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- | No log | 5.0 | 20 | 0.4199 | 1.0 | 0.2017 | 0.3357 | 0.9025 |
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- | No log | 6.0 | 24 | 0.3671 | 1.0 | 0.3866 | 0.5576 | 0.9231 |
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- | No log | 7.0 | 28 | 0.3227 | 0.9 | 0.7563 | 0.8219 | 0.9569 |
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- | No log | 8.0 | 32 | 0.3000 | 0.8333 | 0.8403 | 0.8368 | 0.9625 |
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- | No log | 9.0 | 36 | 0.2802 | 0.8919 | 0.8319 | 0.8609 | 0.9691 |
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- | No log | 10.0 | 40 | 0.2654 | 0.8909 | 0.8235 | 0.8559 | 0.9700 |
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- | No log | 11.0 | 44 | 0.2565 | 0.8443 | 0.8655 | 0.8548 | 0.9691 |
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- | No log | 12.0 | 48 | 0.2418 | 0.8957 | 0.8655 | 0.8803 | 0.9756 |
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- | No log | 13.0 | 52 | 0.2283 | 0.8983 | 0.8908 | 0.8945 | 0.9794 |
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- | No log | 14.0 | 56 | 0.2149 | 0.888 | 0.9328 | 0.9098 | 0.9841 |
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- | No log | 15.0 | 60 | 0.2216 | 0.9115 | 0.8655 | 0.8879 | 0.9775 |
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- | No log | 16.0 | 64 | 0.2106 | 0.9052 | 0.8824 | 0.8936 | 0.9794 |
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- | No log | 17.0 | 68 | 0.1997 | 0.8828 | 0.9496 | 0.9150 | 0.9850 |
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- | No log | 18.0 | 72 | 0.1926 | 0.8828 | 0.9496 | 0.9150 | 0.9850 |
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- | No log | 19.0 | 76 | 0.1840 | 0.8846 | 0.9664 | 0.9237 | 0.9878 |
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- | No log | 20.0 | 80 | 0.1801 | 0.8788 | 0.9748 | 0.9243 | 0.9878 |
78
- | No log | 21.0 | 84 | 0.1737 | 0.8976 | 0.9580 | 0.9268 | 0.9888 |
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- | No log | 22.0 | 88 | 0.1778 | 0.9008 | 0.9160 | 0.9083 | 0.9850 |
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- | No log | 23.0 | 92 | 0.1651 | 0.8915 | 0.9664 | 0.9274 | 0.9888 |
81
- | No log | 24.0 | 96 | 0.1672 | 0.8788 | 0.9748 | 0.9243 | 0.9878 |
82
- | No log | 25.0 | 100 | 0.1614 | 0.8788 | 0.9748 | 0.9243 | 0.9878 |
83
- | No log | 26.0 | 104 | 0.1537 | 0.8923 | 0.9748 | 0.9317 | 0.9897 |
84
- | No log | 27.0 | 108 | 0.1489 | 0.8923 | 0.9748 | 0.9317 | 0.9897 |
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- | No log | 28.0 | 112 | 0.1446 | 0.8992 | 0.9748 | 0.9355 | 0.9906 |
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- | No log | 29.0 | 116 | 0.1417 | 0.8992 | 0.9748 | 0.9355 | 0.9906 |
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- | No log | 30.0 | 120 | 0.1425 | 0.912 | 0.9580 | 0.9344 | 0.9906 |
88
- | No log | 31.0 | 124 | 0.1369 | 0.9915 | 0.9832 | 0.9873 | 0.9972 |
89
- | No log | 32.0 | 128 | 0.1347 | 0.9675 | 1.0 | 0.9835 | 0.9963 |
90
- | No log | 33.0 | 132 | 0.1323 | 0.9754 | 1.0 | 0.9876 | 0.9972 |
91
- | No log | 34.0 | 136 | 0.1308 | 0.9754 | 1.0 | 0.9876 | 0.9972 |
92
- | No log | 35.0 | 140 | 0.1308 | 0.9669 | 0.9832 | 0.975 | 0.9953 |
93
- | No log | 36.0 | 144 | 0.1278 | 0.9669 | 0.9832 | 0.975 | 0.9953 |
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- | No log | 37.0 | 148 | 0.1265 | 0.9583 | 0.9664 | 0.9623 | 0.9944 |
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- | No log | 38.0 | 152 | 0.1291 | 0.9569 | 0.9328 | 0.9447 | 0.9906 |
96
- | No log | 39.0 | 156 | 0.1281 | 0.9569 | 0.9328 | 0.9447 | 0.9906 |
97
- | No log | 40.0 | 160 | 0.1263 | 0.9569 | 0.9328 | 0.9447 | 0.9906 |
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- | No log | 41.0 | 164 | 0.1222 | 0.9576 | 0.9496 | 0.9536 | 0.9925 |
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- | No log | 42.0 | 168 | 0.1167 | 0.9667 | 0.9748 | 0.9707 | 0.9953 |
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- | No log | 43.0 | 172 | 0.1165 | 0.9664 | 0.9664 | 0.9664 | 0.9944 |
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- | No log | 44.0 | 176 | 0.1195 | 0.9569 | 0.9328 | 0.9447 | 0.9906 |
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- | No log | 45.0 | 180 | 0.1186 | 0.9737 | 0.9328 | 0.9528 | 0.9925 |
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- | No log | 46.0 | 184 | 0.1166 | 0.9569 | 0.9328 | 0.9447 | 0.9906 |
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- | No log | 47.0 | 188 | 0.1123 | 0.95 | 0.9580 | 0.9540 | 0.9925 |
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- | No log | 48.0 | 192 | 0.1100 | 0.95 | 0.9580 | 0.9540 | 0.9925 |
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- | No log | 49.0 | 196 | 0.1086 | 0.95 | 0.9580 | 0.9540 | 0.9925 |
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- | No log | 50.0 | 200 | 0.1077 | 0.95 | 0.9580 | 0.9540 | 0.9925 |
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- | No log | 51.0 | 204 | 0.1073 | 0.95 | 0.9580 | 0.9540 | 0.9925 |
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- | No log | 52.0 | 208 | 0.1066 | 0.95 | 0.9580 | 0.9540 | 0.9925 |
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- | No log | 53.0 | 212 | 0.1054 | 0.95 | 0.9580 | 0.9540 | 0.9925 |
111
- | No log | 54.0 | 216 | 0.1075 | 0.9496 | 0.9496 | 0.9496 | 0.9906 |
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- | No log | 55.0 | 220 | 0.1084 | 0.9496 | 0.9496 | 0.9496 | 0.9906 |
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- | No log | 56.0 | 224 | 0.1065 | 0.9576 | 0.9496 | 0.9536 | 0.9916 |
114
- | No log | 57.0 | 228 | 0.1057 | 0.9573 | 0.9412 | 0.9492 | 0.9916 |
115
- | No log | 58.0 | 232 | 0.1053 | 0.9573 | 0.9412 | 0.9492 | 0.9916 |
116
- | No log | 59.0 | 236 | 0.1043 | 0.9573 | 0.9412 | 0.9492 | 0.9916 |
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- | No log | 60.0 | 240 | 0.1033 | 0.9573 | 0.9412 | 0.9492 | 0.9916 |
118
- | No log | 61.0 | 244 | 0.1024 | 0.9573 | 0.9412 | 0.9492 | 0.9916 |
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- | No log | 62.0 | 248 | 0.1003 | 0.9573 | 0.9412 | 0.9492 | 0.9916 |
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- | No log | 63.0 | 252 | 0.1039 | 0.9268 | 0.9580 | 0.9421 | 0.9906 |
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- | No log | 64.0 | 256 | 0.1084 | 0.9194 | 0.9580 | 0.9383 | 0.9888 |
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- | No log | 65.0 | 260 | 0.1062 | 0.912 | 0.9580 | 0.9344 | 0.9888 |
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- | No log | 66.0 | 264 | 0.0981 | 0.9344 | 0.9580 | 0.9461 | 0.9916 |
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- | No log | 67.0 | 268 | 0.0960 | 0.9576 | 0.9496 | 0.9536 | 0.9916 |
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- | No log | 68.0 | 272 | 0.0961 | 0.9573 | 0.9412 | 0.9492 | 0.9916 |
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- | No log | 69.0 | 276 | 0.0951 | 0.9573 | 0.9412 | 0.9492 | 0.9916 |
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- | No log | 70.0 | 280 | 0.0952 | 0.9652 | 0.9328 | 0.9487 | 0.9916 |
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- | No log | 71.0 | 284 | 0.0950 | 0.9652 | 0.9328 | 0.9487 | 0.9916 |
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- | No log | 72.0 | 288 | 0.0943 | 0.9652 | 0.9328 | 0.9487 | 0.9916 |
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- | No log | 73.0 | 292 | 0.0935 | 0.9652 | 0.9328 | 0.9487 | 0.9916 |
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- | No log | 74.0 | 296 | 0.0928 | 0.9652 | 0.9328 | 0.9487 | 0.9916 |
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- | No log | 75.0 | 300 | 0.0922 | 0.9652 | 0.9328 | 0.9487 | 0.9916 |
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- | No log | 76.0 | 304 | 0.0902 | 0.9655 | 0.9412 | 0.9532 | 0.9925 |
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- | No log | 77.0 | 308 | 0.0885 | 0.9658 | 0.9496 | 0.9576 | 0.9934 |
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- | No log | 78.0 | 312 | 0.0869 | 0.9658 | 0.9496 | 0.9576 | 0.9934 |
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- | No log | 79.0 | 316 | 0.0858 | 0.9580 | 0.9580 | 0.9580 | 0.9925 |
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- | No log | 80.0 | 320 | 0.0853 | 0.9580 | 0.9580 | 0.9580 | 0.9925 |
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- | No log | 81.0 | 324 | 0.0852 | 0.9583 | 0.9664 | 0.9623 | 0.9934 |
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- | No log | 82.0 | 328 | 0.0850 | 0.9583 | 0.9664 | 0.9623 | 0.9934 |
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- | No log | 83.0 | 332 | 0.0848 | 0.9583 | 0.9664 | 0.9623 | 0.9934 |
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- | No log | 84.0 | 336 | 0.0846 | 0.9583 | 0.9664 | 0.9623 | 0.9934 |
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- | No log | 85.0 | 340 | 0.0843 | 0.9583 | 0.9664 | 0.9623 | 0.9944 |
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- | No log | 86.0 | 344 | 0.0841 | 0.9583 | 0.9664 | 0.9623 | 0.9944 |
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- | No log | 87.0 | 348 | 0.0839 | 0.9583 | 0.9664 | 0.9623 | 0.9944 |
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- | No log | 88.0 | 352 | 0.0835 | 0.9583 | 0.9664 | 0.9623 | 0.9944 |
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- | No log | 89.0 | 356 | 0.0831 | 0.9583 | 0.9664 | 0.9623 | 0.9944 |
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- | No log | 90.0 | 360 | 0.0828 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 91.0 | 364 | 0.0825 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 92.0 | 368 | 0.0824 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 93.0 | 372 | 0.0822 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 94.0 | 376 | 0.0822 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 95.0 | 380 | 0.0821 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 96.0 | 384 | 0.0821 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 97.0 | 388 | 0.0820 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 98.0 | 392 | 0.0820 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 99.0 | 396 | 0.0820 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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- | No log | 100.0 | 400 | 0.0820 | 0.9583 | 0.9664 | 0.9623 | 0.9953 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0015
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+ - Precision: 1.0
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+ - Recall: 1.0
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+ - F1: 1.0
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+ - Accuracy: 1.0
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 27 | 0.1989 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 2.0 | 54 | 0.1611 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 3.0 | 81 | 0.1334 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 4.0 | 108 | 0.1116 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 5.0 | 135 | 0.0943 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 6.0 | 162 | 0.0804 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 7.0 | 189 | 0.0692 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 8.0 | 216 | 0.0602 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 9.0 | 243 | 0.0528 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 10.0 | 270 | 0.0468 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 11.0 | 297 | 0.0418 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 12.0 | 324 | 0.0376 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 13.0 | 351 | 0.0341 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 14.0 | 378 | 0.0312 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 15.0 | 405 | 0.0287 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 16.0 | 432 | 0.0266 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 17.0 | 459 | 0.0247 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | No log | 18.0 | 486 | 0.0236 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | 0.0904 | 19.0 | 513 | 0.0218 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | 0.0904 | 20.0 | 540 | 0.0203 | 0.0 | 0.0 | 0.0 | 0.9989 |
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+ | 0.0904 | 21.0 | 567 | 0.0156 | 0.8571 | 0.8571 | 0.8571 | 0.9997 |
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+ | 0.0904 | 22.0 | 594 | 0.0142 | 1.0 | 0.8571 | 0.9231 | 0.9998 |
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+ | 0.0904 | 23.0 | 621 | 0.0133 | 1.0 | 0.8571 | 0.9231 | 0.9998 |
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+ | 0.0904 | 24.0 | 648 | 0.0122 | 1.0 | 0.8571 | 0.9231 | 0.9998 |
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+ | 0.0904 | 25.0 | 675 | 0.0107 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 26.0 | 702 | 0.0099 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 27.0 | 729 | 0.0092 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 28.0 | 756 | 0.0086 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 29.0 | 783 | 0.0081 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 30.0 | 810 | 0.0076 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 31.0 | 837 | 0.0074 | 1.0 | 1.0 | 1.0 | 0.9998 |
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+ | 0.0904 | 32.0 | 864 | 0.0073 | 1.0 | 0.8571 | 0.9231 | 0.9998 |
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+ | 0.0904 | 33.0 | 891 | 0.0064 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 34.0 | 918 | 0.0061 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 35.0 | 945 | 0.0058 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 36.0 | 972 | 0.0055 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0904 | 37.0 | 999 | 0.0053 | 1.0 | 1.0 | 1.0 | 1.0 |
95
+ | 0.0122 | 38.0 | 1026 | 0.0050 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0122 | 39.0 | 1053 | 0.0048 | 1.0 | 1.0 | 1.0 | 1.0 |
97
+ | 0.0122 | 40.0 | 1080 | 0.0046 | 1.0 | 1.0 | 1.0 | 1.0 |
98
+ | 0.0122 | 41.0 | 1107 | 0.0044 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0122 | 42.0 | 1134 | 0.0042 | 1.0 | 1.0 | 1.0 | 1.0 |
100
+ | 0.0122 | 43.0 | 1161 | 0.0041 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0122 | 44.0 | 1188 | 0.0039 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0122 | 45.0 | 1215 | 0.0038 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0122 | 46.0 | 1242 | 0.0036 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0122 | 47.0 | 1269 | 0.0035 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0122 | 48.0 | 1296 | 0.0034 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0122 | 49.0 | 1323 | 0.0033 | 1.0 | 1.0 | 1.0 | 1.0 |
107
+ | 0.0122 | 50.0 | 1350 | 0.0032 | 1.0 | 1.0 | 1.0 | 1.0 |
108
+ | 0.0122 | 51.0 | 1377 | 0.0031 | 1.0 | 1.0 | 1.0 | 1.0 |
109
+ | 0.0122 | 52.0 | 1404 | 0.0030 | 1.0 | 1.0 | 1.0 | 1.0 |
110
+ | 0.0122 | 53.0 | 1431 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 |
111
+ | 0.0122 | 54.0 | 1458 | 0.0029 | 1.0 | 1.0 | 1.0 | 1.0 |
112
+ | 0.0122 | 55.0 | 1485 | 0.0028 | 1.0 | 1.0 | 1.0 | 1.0 |
113
+ | 0.0042 | 56.0 | 1512 | 0.0027 | 1.0 | 1.0 | 1.0 | 1.0 |
114
+ | 0.0042 | 57.0 | 1539 | 0.0026 | 1.0 | 1.0 | 1.0 | 1.0 |
115
+ | 0.0042 | 58.0 | 1566 | 0.0026 | 1.0 | 1.0 | 1.0 | 1.0 |
116
+ | 0.0042 | 59.0 | 1593 | 0.0025 | 1.0 | 1.0 | 1.0 | 1.0 |
117
+ | 0.0042 | 60.0 | 1620 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 |
118
+ | 0.0042 | 61.0 | 1647 | 0.0024 | 1.0 | 1.0 | 1.0 | 1.0 |
119
+ | 0.0042 | 62.0 | 1674 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 |
120
+ | 0.0042 | 63.0 | 1701 | 0.0023 | 1.0 | 1.0 | 1.0 | 1.0 |
121
+ | 0.0042 | 64.0 | 1728 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
122
+ | 0.0042 | 65.0 | 1755 | 0.0022 | 1.0 | 1.0 | 1.0 | 1.0 |
123
+ | 0.0042 | 66.0 | 1782 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
124
+ | 0.0042 | 67.0 | 1809 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
125
+ | 0.0042 | 68.0 | 1836 | 0.0021 | 1.0 | 1.0 | 1.0 | 1.0 |
126
+ | 0.0042 | 69.0 | 1863 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
127
+ | 0.0042 | 70.0 | 1890 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
128
+ | 0.0042 | 71.0 | 1917 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 |
129
+ | 0.0042 | 72.0 | 1944 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 |
130
+ | 0.0042 | 73.0 | 1971 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 |
131
+ | 0.0042 | 74.0 | 1998 | 0.0019 | 1.0 | 1.0 | 1.0 | 1.0 |
132
+ | 0.0025 | 75.0 | 2025 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 |
133
+ | 0.0025 | 76.0 | 2052 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 |
134
+ | 0.0025 | 77.0 | 2079 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 |
135
+ | 0.0025 | 78.0 | 2106 | 0.0018 | 1.0 | 1.0 | 1.0 | 1.0 |
136
+ | 0.0025 | 79.0 | 2133 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
137
+ | 0.0025 | 80.0 | 2160 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
138
+ | 0.0025 | 81.0 | 2187 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
139
+ | 0.0025 | 82.0 | 2214 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
140
+ | 0.0025 | 83.0 | 2241 | 0.0017 | 1.0 | 1.0 | 1.0 | 1.0 |
141
+ | 0.0025 | 84.0 | 2268 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
142
+ | 0.0025 | 85.0 | 2295 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
143
+ | 0.0025 | 86.0 | 2322 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
144
+ | 0.0025 | 87.0 | 2349 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
145
+ | 0.0025 | 88.0 | 2376 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
146
+ | 0.0025 | 89.0 | 2403 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
147
+ | 0.0025 | 90.0 | 2430 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
148
+ | 0.0025 | 91.0 | 2457 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
149
+ | 0.0025 | 92.0 | 2484 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
150
+ | 0.0019 | 93.0 | 2511 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
151
+ | 0.0019 | 94.0 | 2538 | 0.0016 | 1.0 | 1.0 | 1.0 | 1.0 |
152
+ | 0.0019 | 95.0 | 2565 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
153
+ | 0.0019 | 96.0 | 2592 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
154
+ | 0.0019 | 97.0 | 2619 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
155
+ | 0.0019 | 98.0 | 2646 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
156
+ | 0.0019 | 99.0 | 2673 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
157
+ | 0.0019 | 100.0 | 2700 | 0.0015 | 1.0 | 1.0 | 1.0 | 1.0 |
158
 
159
 
160
  ### Framework versions
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  size 470051652
 
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