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best_model-sst-2-64-13

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
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@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0984
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- - Accuracy: 0.7969
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  ## Model description
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@@ -50,156 +50,156 @@ 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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- | No log | 1.0 | 4 | 0.8163 | 0.7578 |
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- | No log | 2.0 | 8 | 0.8152 | 0.7578 |
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- | 0.3744 | 3.0 | 12 | 0.8132 | 0.7578 |
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- | 0.3744 | 4.0 | 16 | 0.8101 | 0.7578 |
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- | 0.2942 | 5.0 | 20 | 0.8062 | 0.7578 |
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- | 0.2942 | 6.0 | 24 | 0.8015 | 0.7578 |
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- | 0.2942 | 7.0 | 28 | 0.7956 | 0.7578 |
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- | 0.2802 | 8.0 | 32 | 0.7892 | 0.7578 |
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- | 0.2802 | 9.0 | 36 | 0.7832 | 0.7578 |
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- | 0.2775 | 10.0 | 40 | 0.7759 | 0.7656 |
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- | 0.2775 | 11.0 | 44 | 0.7684 | 0.7656 |
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- | 0.2775 | 12.0 | 48 | 0.7613 | 0.7734 |
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- | 0.2629 | 13.0 | 52 | 0.7535 | 0.7812 |
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- | 0.2629 | 14.0 | 56 | 0.7458 | 0.7812 |
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- | 0.1656 | 15.0 | 60 | 0.7352 | 0.7734 |
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- | 0.1656 | 16.0 | 64 | 0.7253 | 0.7734 |
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- | 0.1656 | 17.0 | 68 | 0.7179 | 0.7656 |
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- | 0.2032 | 18.0 | 72 | 0.7089 | 0.7734 |
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- | 0.2032 | 19.0 | 76 | 0.6996 | 0.7734 |
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- | 0.1397 | 20.0 | 80 | 0.6935 | 0.7734 |
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- | 0.1397 | 21.0 | 84 | 0.6916 | 0.7812 |
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- | 0.1397 | 22.0 | 88 | 0.6879 | 0.7891 |
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- | 0.0971 | 23.0 | 92 | 0.6829 | 0.7891 |
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- | 0.0971 | 24.0 | 96 | 0.6678 | 0.7812 |
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- | 0.0483 | 25.0 | 100 | 0.6503 | 0.7734 |
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- | 0.0483 | 26.0 | 104 | 0.6394 | 0.7891 |
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- | 0.0483 | 27.0 | 108 | 0.6372 | 0.7891 |
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- | 0.0261 | 28.0 | 112 | 0.6343 | 0.7891 |
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- | 0.0261 | 29.0 | 116 | 0.6357 | 0.8047 |
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- | 0.0186 | 30.0 | 120 | 0.6444 | 0.8047 |
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- | 0.0186 | 31.0 | 124 | 0.6516 | 0.8047 |
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- | 0.0186 | 32.0 | 128 | 0.6572 | 0.8125 |
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- | 0.0088 | 33.0 | 132 | 0.6569 | 0.8047 |
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- | 0.0088 | 34.0 | 136 | 0.6629 | 0.8047 |
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- | 0.0081 | 35.0 | 140 | 0.6660 | 0.8125 |
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- | 0.0081 | 36.0 | 144 | 0.6707 | 0.8047 |
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- | 0.0081 | 37.0 | 148 | 0.6774 | 0.8047 |
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- | 0.0056 | 38.0 | 152 | 0.6836 | 0.8047 |
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- | 0.0056 | 39.0 | 156 | 0.6893 | 0.8047 |
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- | 0.0047 | 40.0 | 160 | 0.6946 | 0.8047 |
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- | 0.0047 | 41.0 | 164 | 0.6988 | 0.8125 |
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- | 0.0047 | 42.0 | 168 | 0.7042 | 0.8203 |
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- | 0.0046 | 43.0 | 172 | 0.7088 | 0.8203 |
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- | 0.0046 | 44.0 | 176 | 0.7104 | 0.8203 |
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- | 0.0043 | 45.0 | 180 | 0.7139 | 0.8203 |
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- | 0.0043 | 46.0 | 184 | 0.7181 | 0.8203 |
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- | 0.0043 | 47.0 | 188 | 0.7222 | 0.8203 |
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- | 0.003 | 48.0 | 192 | 0.7264 | 0.8203 |
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- | 0.003 | 49.0 | 196 | 0.7304 | 0.8281 |
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- | 0.0028 | 50.0 | 200 | 0.7345 | 0.8281 |
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- | 0.0028 | 51.0 | 204 | 0.7391 | 0.8281 |
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- | 0.0028 | 52.0 | 208 | 0.7461 | 0.8203 |
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- | 0.0029 | 53.0 | 212 | 0.7573 | 0.8281 |
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- | 0.0029 | 54.0 | 216 | 0.7679 | 0.8125 |
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- | 0.0025 | 55.0 | 220 | 0.7920 | 0.8125 |
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- | 0.0025 | 56.0 | 224 | 0.8067 | 0.8125 |
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- | 0.0025 | 57.0 | 228 | 0.8099 | 0.8125 |
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- | 0.0021 | 58.0 | 232 | 0.8078 | 0.8203 |
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- | 0.0021 | 59.0 | 236 | 0.8036 | 0.8203 |
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- | 0.0018 | 60.0 | 240 | 0.8014 | 0.8125 |
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- | 0.0018 | 61.0 | 244 | 0.8013 | 0.8203 |
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- | 0.0018 | 62.0 | 248 | 0.8025 | 0.8281 |
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- | 0.0017 | 63.0 | 252 | 0.8049 | 0.8281 |
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- | 0.0017 | 64.0 | 256 | 0.8072 | 0.8281 |
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- | 0.0015 | 65.0 | 260 | 0.8103 | 0.8203 |
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- | 0.0015 | 66.0 | 264 | 0.8147 | 0.8203 |
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- | 0.0015 | 67.0 | 268 | 0.8187 | 0.8203 |
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- | 0.0014 | 68.0 | 272 | 0.8226 | 0.8281 |
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- | 0.0014 | 69.0 | 276 | 0.8266 | 0.8281 |
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- | 0.0013 | 70.0 | 280 | 0.8308 | 0.8281 |
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- | 0.0013 | 71.0 | 284 | 0.8349 | 0.8281 |
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- | 0.0013 | 72.0 | 288 | 0.8390 | 0.8281 |
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- | 0.0012 | 73.0 | 292 | 0.8431 | 0.8281 |
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- | 0.0012 | 74.0 | 296 | 0.8472 | 0.8281 |
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- | 0.0011 | 75.0 | 300 | 0.8513 | 0.8281 |
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- | 0.0011 | 76.0 | 304 | 0.8554 | 0.8281 |
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- | 0.0011 | 77.0 | 308 | 0.8595 | 0.8281 |
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- | 0.001 | 78.0 | 312 | 0.8637 | 0.8281 |
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- | 0.001 | 79.0 | 316 | 0.8678 | 0.8281 |
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- | 0.0009 | 80.0 | 320 | 0.8720 | 0.8281 |
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- | 0.0009 | 81.0 | 324 | 0.8762 | 0.8281 |
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- | 0.0009 | 82.0 | 328 | 0.8805 | 0.8203 |
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- | 0.0009 | 83.0 | 332 | 0.8847 | 0.8203 |
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- | 0.0009 | 84.0 | 336 | 0.8889 | 0.8203 |
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- | 0.0008 | 85.0 | 340 | 0.8931 | 0.8203 |
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- | 0.0008 | 86.0 | 344 | 0.8976 | 0.8281 |
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- | 0.0008 | 87.0 | 348 | 0.9021 | 0.8281 |
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- | 0.0007 | 88.0 | 352 | 0.9063 | 0.8281 |
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- | 0.0007 | 89.0 | 356 | 0.9102 | 0.8281 |
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- | 0.0007 | 90.0 | 360 | 0.9137 | 0.8281 |
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- | 0.0007 | 91.0 | 364 | 0.9173 | 0.8281 |
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- | 0.0007 | 92.0 | 368 | 0.9208 | 0.8203 |
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- | 0.0006 | 93.0 | 372 | 0.9244 | 0.8125 |
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- | 0.0006 | 94.0 | 376 | 0.9279 | 0.8125 |
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- | 0.0006 | 95.0 | 380 | 0.9315 | 0.8125 |
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- | 0.0006 | 96.0 | 384 | 0.9351 | 0.8125 |
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- | 0.0006 | 97.0 | 388 | 0.9389 | 0.8125 |
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- | 0.0006 | 98.0 | 392 | 0.9442 | 0.8281 |
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- | 0.0006 | 99.0 | 396 | 0.9564 | 0.8438 |
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- | 0.0005 | 100.0 | 400 | 0.9671 | 0.8359 |
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- | 0.0005 | 101.0 | 404 | 0.9756 | 0.8281 |
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- | 0.0005 | 102.0 | 408 | 0.9801 | 0.8281 |
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- | 0.0005 | 103.0 | 412 | 0.9794 | 0.8438 |
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- | 0.0005 | 104.0 | 416 | 0.9766 | 0.8438 |
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- | 0.0005 | 105.0 | 420 | 0.9766 | 0.8438 |
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- | 0.0005 | 106.0 | 424 | 0.9782 | 0.8359 |
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- | 0.0005 | 107.0 | 428 | 0.9808 | 0.8359 |
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- | 0.0004 | 108.0 | 432 | 0.9840 | 0.8359 |
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- | 0.0004 | 109.0 | 436 | 0.9877 | 0.8359 |
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- | 0.0004 | 110.0 | 440 | 0.9920 | 0.8047 |
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- | 0.0004 | 111.0 | 444 | 0.9988 | 0.7969 |
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- | 0.0004 | 112.0 | 448 | 1.0040 | 0.8047 |
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- | 0.0004 | 113.0 | 452 | 1.0075 | 0.8047 |
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- | 0.0004 | 114.0 | 456 | 1.0109 | 0.8047 |
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- | 0.0003 | 115.0 | 460 | 1.0142 | 0.8047 |
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- | 0.0003 | 116.0 | 464 | 1.0175 | 0.8047 |
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- | 0.0003 | 117.0 | 468 | 1.0209 | 0.8047 |
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- | 0.0003 | 118.0 | 472 | 1.0244 | 0.8047 |
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- | 0.0003 | 119.0 | 476 | 1.0279 | 0.8047 |
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- | 0.0003 | 120.0 | 480 | 1.0317 | 0.8047 |
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- | 0.0003 | 121.0 | 484 | 1.0357 | 0.8047 |
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- | 0.0003 | 122.0 | 488 | 1.0398 | 0.8047 |
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- | 0.0003 | 123.0 | 492 | 1.0436 | 0.8047 |
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- | 0.0003 | 124.0 | 496 | 1.0476 | 0.7969 |
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- | 0.0003 | 125.0 | 500 | 1.0514 | 0.7969 |
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- | 0.0003 | 126.0 | 504 | 1.0552 | 0.7969 |
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- | 0.0003 | 127.0 | 508 | 1.0588 | 0.7969 |
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- | 0.0003 | 128.0 | 512 | 1.0619 | 0.7969 |
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- | 0.0003 | 129.0 | 516 | 1.0648 | 0.7969 |
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- | 0.0002 | 130.0 | 520 | 1.0676 | 0.7969 |
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- | 0.0002 | 131.0 | 524 | 1.0693 | 0.8047 |
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- | 0.0002 | 132.0 | 528 | 1.0721 | 0.7969 |
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- | 0.0003 | 133.0 | 532 | 1.0758 | 0.8047 |
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- | 0.0003 | 134.0 | 536 | 1.0791 | 0.8047 |
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- | 0.0002 | 135.0 | 540 | 1.0807 | 0.7969 |
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- | 0.0002 | 136.0 | 544 | 1.0828 | 0.8047 |
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- | 0.0002 | 137.0 | 548 | 1.0853 | 0.8047 |
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- | 0.0002 | 138.0 | 552 | 1.0879 | 0.8047 |
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- | 0.0002 | 139.0 | 556 | 1.0899 | 0.8047 |
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- | 0.0002 | 140.0 | 560 | 1.0916 | 0.8047 |
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- | 0.0002 | 141.0 | 564 | 1.0930 | 0.8047 |
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- | 0.0002 | 142.0 | 568 | 1.0941 | 0.8047 |
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- | 0.0002 | 143.0 | 572 | 1.0951 | 0.7969 |
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- | 0.0002 | 144.0 | 576 | 1.0960 | 0.7969 |
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- | 0.0002 | 145.0 | 580 | 1.0967 | 0.7969 |
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- | 0.0002 | 146.0 | 584 | 1.0973 | 0.7969 |
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- | 0.0002 | 147.0 | 588 | 1.0978 | 0.7969 |
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- | 0.0002 | 148.0 | 592 | 1.0981 | 0.7969 |
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- | 0.0002 | 149.0 | 596 | 1.0983 | 0.7969 |
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- | 0.0002 | 150.0 | 600 | 1.0984 | 0.7969 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.3339
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+ - Accuracy: 0.8438
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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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+ | No log | 1.0 | 4 | 0.9854 | 0.8594 |
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+ | No log | 2.0 | 8 | 0.9825 | 0.8594 |
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+ | 0.3525 | 3.0 | 12 | 0.9791 | 0.8672 |
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+ | 0.3525 | 4.0 | 16 | 0.9752 | 0.8672 |
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+ | 0.2499 | 5.0 | 20 | 0.9700 | 0.8672 |
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+ | 0.2499 | 6.0 | 24 | 0.9629 | 0.8594 |
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+ | 0.2499 | 7.0 | 28 | 0.9588 | 0.8594 |
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+ | 0.2671 | 8.0 | 32 | 0.9589 | 0.8516 |
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+ | 0.2671 | 9.0 | 36 | 0.9573 | 0.8516 |
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+ | 0.1578 | 10.0 | 40 | 0.9487 | 0.8516 |
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+ | 0.1578 | 11.0 | 44 | 0.9428 | 0.8516 |
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+ | 0.1578 | 12.0 | 48 | 0.9323 | 0.8516 |
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+ | 0.1695 | 13.0 | 52 | 0.9192 | 0.8594 |
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+ | 0.1695 | 14.0 | 56 | 0.9121 | 0.8516 |
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+ | 0.106 | 15.0 | 60 | 0.9055 | 0.8516 |
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+ | 0.106 | 16.0 | 64 | 0.8947 | 0.8594 |
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+ | 0.106 | 17.0 | 68 | 0.8886 | 0.875 |
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+ | 0.1075 | 18.0 | 72 | 0.8914 | 0.8672 |
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+ | 0.1075 | 19.0 | 76 | 0.8882 | 0.8672 |
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+ | 0.0226 | 20.0 | 80 | 0.8871 | 0.8672 |
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+ | 0.0226 | 21.0 | 84 | 0.8825 | 0.8594 |
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+ | 0.0226 | 22.0 | 88 | 0.8841 | 0.8594 |
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+ | 0.0045 | 23.0 | 92 | 0.8858 | 0.8672 |
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+ | 0.0045 | 24.0 | 96 | 0.8902 | 0.875 |
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+ | 0.0108 | 25.0 | 100 | 0.8941 | 0.8672 |
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+ | 0.0108 | 26.0 | 104 | 0.8965 | 0.8672 |
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+ | 0.0108 | 27.0 | 108 | 0.9028 | 0.8594 |
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+ | 0.0242 | 28.0 | 112 | 0.9052 | 0.8594 |
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+ | 0.0242 | 29.0 | 116 | 0.9104 | 0.8594 |
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+ | 0.0004 | 30.0 | 120 | 0.9156 | 0.8594 |
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+ | 0.0004 | 31.0 | 124 | 0.9166 | 0.8594 |
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+ | 0.0004 | 32.0 | 128 | 0.9117 | 0.8594 |
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+ | 0.0004 | 33.0 | 132 | 0.9111 | 0.8594 |
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+ | 0.0004 | 34.0 | 136 | 0.9245 | 0.875 |
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+ | 0.0011 | 35.0 | 140 | 0.9451 | 0.8594 |
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+ | 0.0011 | 36.0 | 144 | 0.9664 | 0.8516 |
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+ | 0.0011 | 37.0 | 148 | 0.9794 | 0.8359 |
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+ | 0.0002 | 38.0 | 152 | 0.9838 | 0.8359 |
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+ | 0.0002 | 39.0 | 156 | 0.9680 | 0.8594 |
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+ | 0.0003 | 40.0 | 160 | 0.9540 | 0.8516 |
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+ | 0.0003 | 41.0 | 164 | 0.9479 | 0.8672 |
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+ | 0.0003 | 42.0 | 168 | 0.9734 | 0.8516 |
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+ | 0.0003 | 43.0 | 172 | 0.9954 | 0.8516 |
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+ | 0.0003 | 44.0 | 176 | 1.0139 | 0.8594 |
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+ | 0.0002 | 45.0 | 180 | 1.0285 | 0.8516 |
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+ | 0.0002 | 46.0 | 184 | 1.0383 | 0.8359 |
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+ | 0.0002 | 47.0 | 188 | 1.0443 | 0.8359 |
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+ | 0.0002 | 48.0 | 192 | 1.0474 | 0.8359 |
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+ | 0.0002 | 49.0 | 196 | 1.0490 | 0.8359 |
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+ | 0.0004 | 50.0 | 200 | 1.0141 | 0.8516 |
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+ | 0.0004 | 51.0 | 204 | 0.9861 | 0.8672 |
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+ | 0.0004 | 52.0 | 208 | 0.9913 | 0.8672 |
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+ | 0.0204 | 53.0 | 212 | 1.0418 | 0.8594 |
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+ | 0.0204 | 54.0 | 216 | 1.0818 | 0.8438 |
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+ | 0.0002 | 55.0 | 220 | 1.1084 | 0.8359 |
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+ | 0.0002 | 56.0 | 224 | 1.1198 | 0.8438 |
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+ | 0.0002 | 57.0 | 228 | 1.1048 | 0.8359 |
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+ | 0.0002 | 58.0 | 232 | 1.0871 | 0.8516 |
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+ | 0.0002 | 59.0 | 236 | 1.0756 | 0.8516 |
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+ | 0.0002 | 60.0 | 240 | 1.0676 | 0.8516 |
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+ | 0.0002 | 61.0 | 244 | 1.0631 | 0.8516 |
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+ | 0.0002 | 62.0 | 248 | 1.0605 | 0.8516 |
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+ | 0.0001 | 63.0 | 252 | 1.0594 | 0.8594 |
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+ | 0.0001 | 64.0 | 256 | 1.0592 | 0.8516 |
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+ | 0.0001 | 65.0 | 260 | 1.0597 | 0.8594 |
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+ | 0.0001 | 66.0 | 264 | 1.0594 | 0.8594 |
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+ | 0.0001 | 67.0 | 268 | 1.0597 | 0.8516 |
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+ | 0.0001 | 68.0 | 272 | 1.0606 | 0.8516 |
121
+ | 0.0001 | 69.0 | 276 | 1.0794 | 0.8516 |
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+ | 0.0003 | 70.0 | 280 | 1.1418 | 0.8438 |
123
+ | 0.0003 | 71.0 | 284 | 1.1868 | 0.8516 |
124
+ | 0.0003 | 72.0 | 288 | 1.2120 | 0.8516 |
125
+ | 0.0001 | 73.0 | 292 | 1.2064 | 0.8516 |
126
+ | 0.0001 | 74.0 | 296 | 1.1566 | 0.8438 |
127
+ | 0.0002 | 75.0 | 300 | 1.1006 | 0.8516 |
128
+ | 0.0002 | 76.0 | 304 | 1.0705 | 0.8516 |
129
+ | 0.0002 | 77.0 | 308 | 1.0654 | 0.8516 |
130
+ | 0.0001 | 78.0 | 312 | 1.0651 | 0.8594 |
131
+ | 0.0001 | 79.0 | 316 | 1.0659 | 0.8594 |
132
+ | 0.0001 | 80.0 | 320 | 1.0674 | 0.8516 |
133
+ | 0.0001 | 81.0 | 324 | 1.0691 | 0.8516 |
134
+ | 0.0001 | 82.0 | 328 | 1.0786 | 0.8516 |
135
+ | 0.0001 | 83.0 | 332 | 1.0875 | 0.8516 |
136
+ | 0.0001 | 84.0 | 336 | 1.0948 | 0.8438 |
137
+ | 0.0001 | 85.0 | 340 | 1.1004 | 0.8438 |
138
+ | 0.0001 | 86.0 | 344 | 1.1058 | 0.8438 |
139
+ | 0.0001 | 87.0 | 348 | 1.1103 | 0.8438 |
140
+ | 0.0001 | 88.0 | 352 | 1.1136 | 0.8438 |
141
+ | 0.0001 | 89.0 | 356 | 1.1162 | 0.8438 |
142
+ | 0.0001 | 90.0 | 360 | 1.1180 | 0.8438 |
143
+ | 0.0001 | 91.0 | 364 | 1.1119 | 0.8438 |
144
+ | 0.0001 | 92.0 | 368 | 1.1084 | 0.8438 |
145
+ | 0.0001 | 93.0 | 372 | 1.1066 | 0.8516 |
146
+ | 0.0001 | 94.0 | 376 | 1.1059 | 0.8516 |
147
+ | 0.0001 | 95.0 | 380 | 1.1059 | 0.8516 |
148
+ | 0.0001 | 96.0 | 384 | 1.1065 | 0.8516 |
149
+ | 0.0001 | 97.0 | 388 | 1.1084 | 0.8516 |
150
+ | 0.0064 | 98.0 | 392 | 1.1955 | 0.8438 |
151
+ | 0.0064 | 99.0 | 396 | 1.2544 | 0.8516 |
152
+ | 0.0001 | 100.0 | 400 | 1.3053 | 0.8359 |
153
+ | 0.0001 | 101.0 | 404 | 1.3606 | 0.8281 |
154
+ | 0.0001 | 102.0 | 408 | 1.3399 | 0.8281 |
155
+ | 0.0068 | 103.0 | 412 | 1.2648 | 0.8516 |
156
+ | 0.0068 | 104.0 | 416 | 1.1161 | 0.8516 |
157
+ | 0.0001 | 105.0 | 420 | 1.0830 | 0.8594 |
158
+ | 0.0001 | 106.0 | 424 | 1.1095 | 0.8672 |
159
+ | 0.0001 | 107.0 | 428 | 1.0817 | 0.8672 |
160
+ | 0.0139 | 108.0 | 432 | 1.1057 | 0.8516 |
161
+ | 0.0139 | 109.0 | 436 | 1.1392 | 0.8438 |
162
+ | 0.0001 | 110.0 | 440 | 1.1623 | 0.8438 |
163
+ | 0.0001 | 111.0 | 444 | 1.1707 | 0.8438 |
164
+ | 0.0001 | 112.0 | 448 | 1.1766 | 0.8438 |
165
+ | 0.0001 | 113.0 | 452 | 1.1808 | 0.8516 |
166
+ | 0.0001 | 114.0 | 456 | 1.1826 | 0.8516 |
167
+ | 0.0001 | 115.0 | 460 | 1.1809 | 0.8438 |
168
+ | 0.0001 | 116.0 | 464 | 1.1380 | 0.8438 |
169
+ | 0.0001 | 117.0 | 468 | 1.1289 | 0.8594 |
170
+ | 0.0001 | 118.0 | 472 | 1.1853 | 0.8594 |
171
+ | 0.0001 | 119.0 | 476 | 1.2030 | 0.8594 |
172
+ | 0.0001 | 120.0 | 480 | 1.1913 | 0.8594 |
173
+ | 0.0001 | 121.0 | 484 | 1.1660 | 0.8672 |
174
+ | 0.0001 | 122.0 | 488 | 1.1591 | 0.8594 |
175
+ | 0.0001 | 123.0 | 492 | 1.1678 | 0.8438 |
176
+ | 0.0001 | 124.0 | 496 | 1.1800 | 0.8516 |
177
+ | 0.0001 | 125.0 | 500 | 1.1896 | 0.8516 |
178
+ | 0.0001 | 126.0 | 504 | 1.1972 | 0.8516 |
179
+ | 0.0001 | 127.0 | 508 | 1.2034 | 0.8516 |
180
+ | 0.0001 | 128.0 | 512 | 1.2074 | 0.8438 |
181
+ | 0.0001 | 129.0 | 516 | 1.2104 | 0.8438 |
182
+ | 0.0 | 130.0 | 520 | 1.2126 | 0.8438 |
183
+ | 0.0 | 131.0 | 524 | 1.1920 | 0.8672 |
184
+ | 0.0 | 132.0 | 528 | 1.2214 | 0.8516 |
185
+ | 0.0007 | 133.0 | 532 | 1.2321 | 0.8516 |
186
+ | 0.0007 | 134.0 | 536 | 1.2382 | 0.8516 |
187
+ | 0.0001 | 135.0 | 540 | 1.2297 | 0.8516 |
188
+ | 0.0001 | 136.0 | 544 | 1.1786 | 0.8516 |
189
+ | 0.0001 | 137.0 | 548 | 1.2126 | 0.8516 |
190
+ | 0.0001 | 138.0 | 552 | 1.2706 | 0.8516 |
191
+ | 0.0001 | 139.0 | 556 | 1.2978 | 0.8516 |
192
+ | 0.0 | 140.0 | 560 | 1.3119 | 0.8516 |
193
+ | 0.0 | 141.0 | 564 | 1.3222 | 0.8438 |
194
+ | 0.0 | 142.0 | 568 | 1.3290 | 0.8438 |
195
+ | 0.0 | 143.0 | 572 | 1.3333 | 0.8438 |
196
+ | 0.0 | 144.0 | 576 | 1.3357 | 0.8438 |
197
+ | 0.0 | 145.0 | 580 | 1.3371 | 0.8438 |
198
+ | 0.0 | 146.0 | 584 | 1.3371 | 0.8438 |
199
+ | 0.0 | 147.0 | 588 | 1.3353 | 0.8438 |
200
+ | 0.0001 | 148.0 | 592 | 1.3344 | 0.8438 |
201
+ | 0.0001 | 149.0 | 596 | 1.3340 | 0.8438 |
202
+ | 0.0 | 150.0 | 600 | 1.3339 | 0.8438 |
203
 
204
 
205
  ### Framework versions