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efficientvit-ena24_MD

Image Classification
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timm
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efficientvit-ena24

This model is a fine-tuned version of timm/efficientvit_b0.r224_in1k on the mbiarreta/ena24_MD dataset. It achieves the following results on the evaluation set:

  • Loss: 2.2599
  • Accuracy: 0.4824
  • F1: 0.4440
  • Acc American black bear: 0.9375
  • Acc American crow: 0.5781
  • Acc Bird: 0.3913
  • Acc Bobcat: 0.4118
  • Acc Chicken: 0.12
  • Acc Coyote: 0.2857
  • Acc Dog: 0.3462
  • Acc Domestic cat: 0.14
  • Acc Eastern chipmunk: 0.8438
  • Acc Eastern cottontail: 0.5294
  • Acc Eastern fox squirrel: 0.6
  • Acc Eastern gray squirrel: 0.4688
  • Acc Grey fox: 0.1364
  • Acc Horse: 0.0
  • Acc Northern raccoon: 0.3333
  • Acc Red fox: 0.5
  • Acc Striped skunk: 0.6667
  • Acc Virginia opossum: 0.8219
  • Acc White Tailed Deer: 0.7692
  • Acc Wild turkey: 0.3953
  • Acc Woodchuck: 0.4286
  • F1 American black bear: 0.6406
  • F1 American crow: 0.7255
  • F1 Bird: 0.3
  • F1 Bobcat: 0.2718
  • F1 Chicken: 0.1314
  • F1 Coyote: 0.4
  • F1 Dog: 0.45
  • F1 Domestic cat: 0.1157
  • F1 Eastern chipmunk: 0.8060
  • F1 Eastern cottontail: 0.4615
  • F1 Eastern fox squirrel: 0.4773
  • F1 Eastern gray squirrel: 0.5455
  • F1 Grey fox: 0.1690
  • F1 Horse: 0.0
  • F1 Northern raccoon: 0.3846
  • F1 Red fox: 0.4773
  • F1 Striped skunk: 0.625
  • F1 Virginia opossum: 0.8451
  • F1 White Tailed Deer: 0.5556
  • F1 Wild turkey: 0.4928
  • F1 Woodchuck: 0.45

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: 0.0002
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 7
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Acc American black bear Acc American crow Acc Bird Acc Bobcat Acc Chicken Acc Coyote Acc Dog Acc Domestic cat Acc Eastern chipmunk Acc Eastern cottontail Acc Eastern fox squirrel Acc Eastern gray squirrel Acc Grey fox Acc Horse Acc Northern raccoon Acc Red fox Acc Striped skunk Acc Virginia opossum Acc White Tailed Deer Acc Wild turkey Acc Woodchuck F1 American black bear F1 American crow F1 Bird F1 Bobcat F1 Chicken F1 Coyote F1 Dog F1 Domestic cat F1 Eastern chipmunk F1 Eastern cottontail F1 Eastern fox squirrel F1 Eastern gray squirrel F1 Grey fox F1 Horse F1 Northern raccoon F1 Red fox F1 Striped skunk F1 Virginia opossum F1 White Tailed Deer F1 Wild turkey F1 Woodchuck
1.7495 0.1259 100 2.8202 0.1963 0.1387 0.8125 0.125 0.0 0.0294 0.0 0.1429 0.0385 0.0 0.0625 0.3235 0.1429 0.7812 0.1818 0.0 0.0667 0.1190 0.2333 0.4247 0.0256 0.0233 0.0 0.3284 0.2078 0.0 0.0571 0.0 0.1639 0.0732 0.0 0.0635 0.3385 0.1818 0.1493 0.1404 0.0 0.1176 0.1786 0.28 0.5536 0.0455 0.0333 0.0
1.3081 0.2519 200 2.7355 0.2266 0.1655 0.9062 0.1406 0.0 0.0 0.1467 0.5143 0.1282 0.0 0.0 0.0882 0.2286 0.625 0.25 0.0 0.0 0.2619 0.0 0.2055 0.1282 0.3256 0.0476 0.4394 0.2323 0.0 0.0 0.1028 0.5294 0.1389 0.0 0.0 0.1395 0.2963 0.1762 0.2115 0.0 0.0 0.2933 0.0 0.3297 0.0769 0.4179 0.0909
1.2023 0.3778 300 2.8526 0.2725 0.2011 0.7708 0.1953 0.0435 0.0294 0.0267 0.0 0.0769 0.04 0.6875 0.0 0.2857 0.125 0.0909 0.0 0.2 0.3571 0.6667 0.9589 0.0 0.2326 0.3333 0.4625 0.3205 0.0541 0.0488 0.0392 0.0 0.1224 0.0563 0.6471 0.0 0.3333 0.1333 0.0762 0.0 0.0968 0.3614 0.5 0.5224 0.0 0.3333 0.1148
0.9314 0.5038 400 2.6854 0.2734 0.1956 0.6042 0.5 0.0 0.0294 0.0 0.2857 0.0256 0.02 0.0 0.1765 0.4 0.0 0.1591 0.0 0.0 0.0476 0.2333 0.6301 0.7692 0.7442 0.0 0.4854 0.5 0.0 0.0488 0.0 0.4167 0.0444 0.0286 0.0 0.1379 0.5 0.0 0.1443 0.0 0.0 0.0755 0.3415 0.736 0.5 0.1488 0.0
0.8631 0.6297 500 2.3217 0.4033 0.3350 0.8333 0.6562 0.6087 0.1765 0.1733 0.1143 0.1667 0.14 0.6875 0.1176 0.4286 0.0312 0.2727 0.0 0.0333 0.5714 0.4333 0.8493 0.5641 0.1628 0.4286 0.6584 0.6364 0.2979 0.2182 0.1287 0.1702 0.2364 0.14 0.7719 0.2105 0.5357 0.0571 0.1935 0.0 0.0444 0.48 0.4815 0.7848 0.4490 0.2642 0.2769
0.7006 0.7557 600 2.4787 0.3877 0.2936 0.5208 0.9219 0.3478 0.3529 0.0933 0.7714 0.2051 0.0 0.875 0.1765 0.5429 0.0312 0.2273 0.0 0.0 0.0714 0.1333 0.8767 0.1026 0.2558 0.4286 0.5714 0.8369 0.3404 0.1132 0.1217 0.2547 0.3299 0.0 0.6022 0.2449 0.6032 0.0606 0.1869 0.0 0.0 0.1224 0.2162 0.7619 0.1481 0.3607 0.2903
1.0206 0.8816 700 2.5881 0.3867 0.3037 0.9375 0.6719 0.3913 0.2059 0.0667 0.4857 0.0385 0.04 0.5312 0.0294 0.2286 0.0938 0.4091 0.0 0.1 0.4762 0.1333 0.8904 0.5385 0.2558 0.2857 0.4891 0.7783 0.3 0.14 0.0943 0.3617 0.0698 0.0571 0.6939 0.0541 0.3478 0.1667 0.2118 0.0 0.125 0.4494 0.2353 0.6952 0.5185 0.3860 0.2034
0.6393 1.0076 800 2.5619 0.3906 0.3520 0.4062 0.3125 0.3478 0.3235 0.04 0.0286 0.7821 0.04 0.8438 0.2353 0.7143 0.4375 0.1591 0.0 0.5 0.5714 0.5333 0.8356 0.4359 0.3023 0.3810 0.4815 0.4651 0.3265 0.1982 0.075 0.0556 0.5810 0.0360 0.7297 0.2353 0.3125 0.4444 0.1818 0.0 0.4348 0.3310 0.6038 0.8472 0.4198 0.3133 0.32
0.4724 1.1335 900 2.6886 0.3359 0.2632 0.8646 0.3281 0.3478 0.0588 0.04 0.0 0.0513 0.0 0.6875 0.5 0.1143 0.0938 0.3409 0.0 0.3 0.5238 0.0667 0.9178 0.3846 0.5814 0.0476 0.4689 0.4941 0.3137 0.1053 0.0492 0.0 0.0952 0.0 0.6984 0.2957 0.1951 0.1463 0.2069 0.0 0.3214 0.5116 0.125 0.8816 0.3125 0.2146 0.0909
0.5096 1.2594 1000 2.7225 0.3701 0.3186 0.375 0.7188 0.3043 0.2941 0.0267 0.0857 0.1154 0.22 0.5938 0.6471 0.6857 0.5938 0.0682 0.0 0.1 0.4286 0.4333 0.9178 0.0 0.2791 0.4286 0.5333 0.7797 0.2979 0.1887 0.0482 0.0645 0.2069 0.1095 0.6786 0.2558 0.4571 0.4 0.1224 0.0 0.1091 0.4557 0.5306 0.7403 0.0 0.4211 0.2903
0.3412 1.3854 1100 2.8234 0.3457 0.3339 0.4375 0.2891 0.6957 0.0882 0.04 0.4857 0.0641 0.02 0.6875 0.5882 0.6286 0.8125 0.1364 0.0 0.1 0.0952 0.6333 0.6164 0.9744 0.3953 0.3810 0.5350 0.4431 0.2462 0.1071 0.0368 0.5763 0.1176 0.0333 0.7719 0.3448 0.3411 0.2781 0.15 0.0 0.1154 0.16 0.5758 0.7317 0.4720 0.5312 0.4444
0.4115 1.5113 1200 2.4271 0.4746 0.4125 0.9271 0.5547 0.6522 0.1176 0.08 0.6286 0.2436 0.06 0.9375 0.2647 0.6571 0.3438 0.2727 0.0 0.4 0.3333 0.6667 0.9315 0.6410 0.5349 0.4762 0.712 0.7065 0.3297 0.1455 0.0930 0.5116 0.3220 0.0833 0.6522 0.3273 0.5897 0.4151 0.2759 0.0 0.3871 0.3733 0.6061 0.7120 0.5319 0.5610 0.3279
0.6126 1.6373 1300 2.4840 0.4473 0.3804 0.8125 0.5234 0.6087 0.0294 0.0667 0.3143 0.3846 0.08 0.9688 0.2059 0.6857 0.1562 0.1591 0.0 0.3667 0.3333 0.7333 0.9178 0.5385 0.6744 0.4762 0.7290 0.6634 0.3146 0.0385 0.0877 0.3056 0.4615 0.0920 0.7126 0.3182 0.4706 0.2703 0.1443 0.0 0.3056 0.3733 0.352 0.8758 0.3784 0.6304 0.4651
0.3078 1.7632 1400 2.2599 0.4824 0.4440 0.9375 0.5781 0.3913 0.4118 0.12 0.2857 0.3462 0.14 0.8438 0.5294 0.6 0.4688 0.1364 0.0 0.3333 0.5 0.6667 0.8219 0.7692 0.3953 0.4286 0.6406 0.7255 0.3 0.2718 0.1314 0.4 0.45 0.1157 0.8060 0.4615 0.4773 0.5455 0.1690 0.0 0.3846 0.4773 0.625 0.8451 0.5556 0.4928 0.45
0.3732 1.8892 1500 2.6001 0.5088 0.4613 0.7708 0.6328 0.9130 0.2059 0.0267 0.3714 0.6282 0.08 0.9062 0.2941 0.6 0.5625 0.0909 0.0 0.4 0.6905 0.6667 0.9178 0.7179 0.5349 0.4286 0.7150 0.7570 0.35 0.1795 0.0412 0.4727 0.6405 0.0879 0.8169 0.4 0.6 0.6545 0.1111 0.0 0.2857 0.5043 0.6349 0.8535 0.3972 0.6389 0.5455
0.2557 2.0151 1600 2.9190 0.3965 0.3423 0.9271 0.5234 0.2609 0.1176 0.0533 0.5143 0.1282 0.0 0.6562 0.2059 0.2857 0.0 0.3182 0.0 0.3333 0.4048 0.4667 0.8493 0.7692 0.3256 0.4286 0.4623 0.6802 0.2727 0.0851 0.0816 0.5143 0.1786 0.0 0.7778 0.2745 0.4255 0.0 0.1918 0.0 0.2941 0.3953 0.3889 0.8671 0.48 0.4179 0.4
0.2271 2.1411 1700 2.7502 0.4619 0.4165 0.9271 0.6094 0.7826 0.0294 0.2 0.2571 0.0513 0.12 0.7188 0.3235 0.5714 0.1875 0.2955 0.0 0.4333 0.4048 0.5667 0.9041 0.7179 0.6512 0.5238 0.6593 0.7027 0.4337 0.0513 0.1523 0.2727 0.0833 0.1132 0.7797 0.4 0.6780 0.3158 0.25 0.0 0.5 0.4928 0.5484 0.8098 0.5437 0.6222 0.3385
0.1942 2.2670 1800 2.8448 0.4697 0.4437 0.9479 0.4297 0.7391 0.5 0.1333 0.3143 0.3846 0.06 0.6875 0.1765 0.6 0.3438 0.2045 0.0 0.6 0.3333 0.8333 0.8493 0.5641 0.5581 0.6190 0.5322 0.5759 0.4416 0.425 0.1439 0.3014 0.5172 0.0698 0.7586 0.2609 0.6774 0.4889 0.2687 0.0 0.3303 0.4667 0.5263 0.8794 0.55 0.6316 0.4727
0.1128 2.3929 1900 2.8647 0.4980 0.4378 0.9062 0.7891 0.9130 0.2941 0.0533 0.3143 0.2308 0.04 0.875 0.4706 0.6286 0.4062 0.1591 0.0 0.3 0.4524 0.7333 0.9452 0.2564 0.7209 0.4762 0.6797 0.8632 0.4038 0.2128 0.0734 0.2444 0.3462 0.0556 0.8 0.5 0.6027 0.5652 0.1538 0.0 0.3273 0.4810 0.7719 0.8961 0.2632 0.4882 0.4651
0.1483 2.5189 2000 2.6665 0.5322 0.4788 0.9271 0.7031 0.8261 0.4706 0.1067 0.5429 0.3590 0.04 0.9062 0.4118 0.6286 0.7188 0.2273 0.0 0.1667 0.5238 0.8 0.9726 0.4872 0.5814 0.4762 0.7355 0.7930 0.4176 0.3902 0.1176 0.6129 0.4746 0.0597 0.7838 0.4746 0.6027 0.6133 0.1852 0.0 0.2381 0.5116 0.7385 0.7889 0.4524 0.5882 0.4762
0.4629 2.6448 2100 2.8979 0.5068 0.4758 0.9479 0.5859 0.7391 0.2647 0.04 0.4857 0.3333 0.02 0.75 0.5 0.7429 0.5938 0.4318 0.0 0.3667 0.4762 0.6333 0.8767 0.6410 0.5349 0.6190 0.6341 0.7317 0.4 0.24 0.0522 0.5667 0.4771 0.0241 0.8421 0.3542 0.7027 0.6909 0.3423 0.0 0.3793 0.5634 0.7037 0.8533 0.5 0.5823 0.3514
0.175 2.7708 2200 2.5847 0.5479 0.4691 0.8438 0.9297 0.7826 0.0882 0.08 0.2571 0.7949 0.12 0.7812 0.4706 0.6857 0.7188 0.25 0.0 0.4667 0.4762 0.3333 0.9041 0.4359 0.4651 0.5238 0.7826 0.9297 0.3673 0.1 0.1091 0.3673 0.6966 0.1143 0.8065 0.4384 0.7273 0.6389 0.2821 0.0 0.3836 0.5333 0.4762 0.7857 0.3333 0.5556 0.4231
0.2218 2.8967 2300 2.7785 0.5400 0.4982 0.8646 0.6797 0.6957 0.3529 0.16 0.6857 0.4872 0.04 0.8438 0.3529 0.6857 0.625 0.2045 0.0 0.4 0.4286 0.8667 0.8767 0.9231 0.4884 0.4762 0.7793 0.7982 0.2783 0.3333 0.1387 0.7059 0.5468 0.0702 0.7606 0.4528 0.6857 0.5634 0.2609 0.0 0.375 0.4932 0.5977 0.8649 0.5255 0.6269 0.6061
0.0857 3.0227 2400 2.8899 0.5527 0.4934 0.9792 0.8359 0.6957 0.3824 0.0667 0.6571 0.5769 0.02 0.8438 0.2941 0.6857 0.5312 0.1591 0.0 0.4 0.5714 0.5333 0.8630 0.7949 0.4419 0.5714 0.6225 0.8807 0.3951 0.4062 0.0909 0.6667 0.5769 0.0328 0.8710 0.4348 0.5275 0.6182 0.1667 0.0 0.4444 0.4706 0.6531 0.875 0.5962 0.5 0.5333
0.2255 3.1486 2500 2.7107 0.5566 0.4745 0.9271 0.7578 0.3478 0.2941 0.1467 0.8286 0.8462 0.04 0.9688 0.5294 0.6857 0.4688 0.4318 0.0 0.2667 0.4762 0.6 0.9589 0.2051 0.3488 0.5714 0.712 0.8326 0.2759 0.3636 0.2056 0.5686 0.7135 0.0606 0.8052 0.5538 0.5217 0.5263 0.3248 0.0 0.2963 0.5063 0.6545 0.8537 0.2286 0.5085 0.4528
0.2458 3.2746 2600 2.9639 0.5176 0.4577 0.9479 0.6172 0.4348 0.3824 0.1333 0.8 0.5641 0.0 0.8438 0.2059 0.6857 0.3438 0.2955 0.0 0.1667 0.5 0.7667 0.9178 0.7692 0.4186 0.4286 0.6254 0.7453 0.2899 0.3059 0.1460 0.6914 0.5906 0.0 0.8182 0.2979 0.6316 0.5116 0.2364 0.0 0.25 0.5060 0.6389 0.8590 0.5217 0.5373 0.4091
0.0796 3.4005 2700 2.8778 0.5635 0.5053 0.9375 0.7969 0.7826 0.5 0.1067 0.4286 0.4615 0.02 0.9375 0.5882 0.7143 0.5625 0.2273 0.0 0.3333 0.5952 0.7333 0.8904 0.9231 0.4651 0.4286 0.7563 0.8755 0.3711 0.3366 0.1231 0.5660 0.576 0.0282 0.8 0.5714 0.6173 0.7059 0.2985 0.0 0.3922 0.5 0.6377 0.8784 0.5669 0.5714 0.4390
0.2234 3.5264 2800 3.0540 0.5576 0.4922 0.9375 0.7188 0.8261 0.2941 0.0667 0.7429 0.6282 0.02 0.9062 0.5294 0.6571 0.75 0.3636 0.0 0.2667 0.4048 0.6667 0.8904 0.9231 0.3488 0.3810 0.6844 0.8070 0.3585 0.2532 0.1031 0.7536 0.6853 0.0370 0.8406 0.4444 0.6667 0.6234 0.3721 0.0 0.3019 0.4474 0.6897 0.8075 0.5625 0.4762 0.4211
0.0656 3.6524 2900 2.9764 0.5684 0.4927 0.9375 0.8125 0.6087 0.3824 0.0533 0.3714 0.6795 0.06 0.9688 0.5294 0.6286 0.5 0.2273 0.0 0.4333 0.6429 0.5 0.9315 0.8205 0.5814 0.5238 0.7171 0.8703 0.35 0.3714 0.0988 0.5 0.7518 0.0845 0.7848 0.4737 0.7097 0.5818 0.2564 0.0 0.3824 0.5143 0.6 0.8395 0.4571 0.5952 0.4074
0.2256 3.7783 3000 3.1337 0.5420 0.4856 0.9375 0.6953 0.5652 0.5 0.0667 0.5714 0.4231 0.02 0.8438 0.4706 0.6571 0.7188 0.25 0.0 0.4 0.5 0.7 0.9726 0.8718 0.4651 0.3810 0.6897 0.8203 0.3291 0.3469 0.0847 0.6061 0.5116 0.0286 0.7714 0.4638 0.6479 0.6216 0.2933 0.0 0.3380 0.5060 0.7119 0.8452 0.6296 0.5797 0.3721
0.0854 3.9043 3100 3.0200 0.5732 0.5050 0.8854 0.875 0.7826 0.3235 0.0667 0.4 0.5 0.1 0.875 0.3824 0.7143 0.7188 0.25 0.0 0.4333 0.7143 0.4333 0.9726 0.8718 0.6512 0.4286 0.7798 0.9180 0.3871 0.3333 0.0980 0.5091 0.5735 0.1190 0.8485 0.4062 0.6849 0.7667 0.2683 0.0 0.3824 0.5941 0.5778 0.8554 0.4892 0.6667 0.3462
0.0079 4.0302 3200 2.8893 0.5693 0.5022 0.9375 0.8438 0.6957 0.3824 0.0667 0.7429 0.3846 0.02 0.9688 0.4412 0.6857 0.5938 0.2273 0.0 0.4333 0.6190 0.6667 0.9589 0.6667 0.7209 0.4286 0.7531 0.8963 0.4324 0.3133 0.0826 0.5591 0.5172 0.0299 0.7949 0.5 0.6234 0.6667 0.2353 0.0 0.3824 0.5778 0.5970 0.8537 0.5843 0.6966 0.45
0.1127 4.1562 3300 3.1284 0.5684 0.5108 0.9375 0.7422 0.7826 0.2941 0.1067 0.6286 0.5897 0.06 0.9375 0.5294 0.7143 0.6875 0.1364 0.0 0.4 0.5952 0.6333 0.9178 0.8462 0.4884 0.5714 0.7287 0.8444 0.3871 0.25 0.1495 0.6286 0.6434 0.0779 0.8333 0.5217 0.6579 0.7586 0.1765 0.0 0.3529 0.5102 0.7308 0.8758 0.5739 0.6176 0.4068
0.0115 4.2821 3400 3.3688 0.5391 0.4841 0.9271 0.7422 0.8261 0.3824 0.0933 0.8286 0.1923 0.0 0.8125 0.2353 0.6571 0.6562 0.2273 0.0 0.3333 0.6190 0.6 0.9726 0.8462 0.6279 0.5714 0.6473 0.8370 0.4222 0.3171 0.1228 0.6667 0.3093 0.0 0.8525 0.3404 0.7077 0.7241 0.1786 0.0 0.3704 0.5253 0.6102 0.8712 0.6286 0.6667 0.3692
0.0615 4.4081 3500 3.1003 0.5771 0.5119 0.9271 0.8047 0.7826 0.3235 0.1067 0.7714 0.6154 0.02 0.9375 0.4412 0.7429 0.625 0.25 0.0 0.4 0.5714 0.6667 0.9589 0.8205 0.3953 0.4286 0.7177 0.8766 0.3789 0.3548 0.1524 0.7606 0.6115 0.0333 0.9091 0.4918 0.5417 0.7018 0.2472 0.0 0.375 0.5 0.7407 0.8696 0.6038 0.5312 0.3529
0.0625 4.5340 3600 3.1293 0.5693 0.5022 0.9271 0.75 0.7391 0.5 0.08 0.5714 0.5897 0.02 0.9688 0.5588 0.7429 0.6562 0.1591 0.0 0.4 0.6190 0.6333 0.9726 0.8974 0.3721 0.3810 0.6768 0.8421 0.4048 0.3736 0.1290 0.6349 0.6866 0.0328 0.8052 0.5588 0.5417 0.7241 0.1892 0.0 0.4528 0.4815 0.7308 0.8765 0.6140 0.5 0.2909
0.0079 4.6599 3700 3.1365 0.5801 0.5172 0.8854 0.8828 0.8696 0.3529 0.08 0.6857 0.4744 0.04 0.9688 0.5588 0.6857 0.7812 0.2727 0.0 0.3333 0.4286 0.7333 0.9178 0.9231 0.4651 0.5238 0.7113 0.8863 0.4124 0.3288 0.1017 0.75 0.6066 0.0615 0.8267 0.5135 0.6076 0.7042 0.2727 0.0 0.3390 0.4615 0.6984 0.8816 0.6667 0.5634 0.4681
0.0124 4.7859 3800 3.0567 0.5732 0.5129 0.9375 0.7969 0.7826 0.2941 0.0933 0.7143 0.4872 0.04 0.9375 0.5588 0.7429 0.6875 0.1818 0.0 0.4333 0.6667 0.6333 0.9041 0.9231 0.4651 0.3810 0.7143 0.8681 0.4390 0.2740 0.1207 0.7463 0.5938 0.0625 0.8696 0.5672 0.5652 0.7333 0.1882 0.0 0.4727 0.5333 0.6441 0.8516 0.6154 0.5634 0.3478
0.0592 4.9118 3900 2.9430 0.6172 0.5467 0.9583 0.8438 0.8261 0.3235 0.1467 0.7429 0.6538 0.06 0.9688 0.5294 0.7429 0.6875 0.1136 0.0 0.4667 0.6905 0.8 0.9589 0.9487 0.5814 0.4762 0.7667 0.9 0.4634 0.3385 0.1930 0.7536 0.7286 0.0952 0.8267 0.5455 0.6842 0.7333 0.1449 0.0 0.3457 0.5859 0.7385 0.8917 0.6325 0.7042 0.4082
0.0176 5.0378 4000 3.1490 0.5967 0.5418 0.9271 0.7266 0.7391 0.5 0.1333 0.7429 0.7308 0.04 0.9375 0.5882 0.7429 0.7188 0.1818 0.0 0.3667 0.6667 0.6 0.9589 0.9487 0.4651 0.4286 0.7149 0.8341 0.3778 0.4533 0.1613 0.7647 0.7651 0.0656 0.8696 0.5128 0.7123 0.7302 0.1905 0.0 0.4314 0.5385 0.7059 0.9211 0.5827 0.5970 0.45
0.0219 5.1637 4100 3.0442 0.6045 0.5410 0.9271 0.8984 0.8696 0.4412 0.1467 0.7714 0.5769 0.06 0.9062 0.5588 0.7429 0.7812 0.2727 0.0 0.3333 0.5238 0.6333 0.9589 0.7949 0.4884 0.4762 0.7479 0.8949 0.4494 0.3896 0.1818 0.7606 0.6870 0.0909 0.8286 0.5135 0.6582 0.7246 0.2667 0.0 0.3571 0.5176 0.7308 0.8917 0.5962 0.6087 0.4651
0.0353 5.2897 4200 3.1909 0.5908 0.5274 0.9583 0.8125 0.7391 0.4118 0.1333 0.6 0.5513 0.06 0.9062 0.5294 0.6857 0.75 0.1818 0.0 0.4 0.5952 0.6667 0.9589 0.9487 0.6047 0.3810 0.7603 0.8889 0.3736 0.4 0.1587 0.6885 0.6466 0.0923 0.8286 0.5625 0.6667 0.7059 0.1975 0.0 0.3529 0.4902 0.7018 0.9150 0.6325 0.65 0.3636
0.0041 5.4156 4300 3.2985 0.5918 0.5253 0.9479 0.875 0.8261 0.6765 0.12 0.5143 0.4615 0.0 0.875 0.5588 0.7429 0.6875 0.1364 0.0 0.3667 0.7619 0.6333 0.9452 0.9487 0.4884 0.3810 0.7054 0.9218 0.3838 0.5542 0.1552 0.6102 0.6 0.0 0.875 0.6441 0.6667 0.6567 0.1739 0.0 0.4 0.5333 0.6667 0.8734 0.6116 0.6 0.4
0.1064 5.5416 4400 3.2297 0.6074 0.5383 0.9375 0.8516 0.7826 0.5294 0.12 0.4571 0.6923 0.08 0.9062 0.5588 0.7429 0.8125 0.0909 0.0 0.4333 0.7857 0.6 0.9589 0.9487 0.4651 0.4286 0.7143 0.9046 0.3673 0.4337 0.18 0.5926 0.7660 0.1143 0.8788 0.5672 0.7123 0.7647 0.1194 0.0 0.4262 0.5641 0.6792 0.8861 0.6271 0.5882 0.4186
0.0027 5.6675 4500 3.2256 0.6064 0.5373 0.9583 0.875 0.7826 0.5588 0.1467 0.5143 0.6282 0.02 0.9688 0.5294 0.7429 0.7188 0.2045 0.0 0.4333 0.5714 0.6 0.9589 1.0 0.5116 0.3810 0.7541 0.9180 0.4045 0.3838 0.1897 0.6545 0.7313 0.0339 0.8732 0.5806 0.5778 0.7419 0.2432 0.0 0.3768 0.5 0.6792 0.9211 0.624 0.6377 0.4571
0.0009 5.7935 4600 3.2828 0.5957 0.5257 0.9375 0.8672 0.7391 0.3529 0.1467 0.6 0.6410 0.04 0.9375 0.5294 0.7714 0.5625 0.2045 0.0 0.5 0.6190 0.6333 0.9589 0.9487 0.4419 0.3810 0.7229 0.9174 0.3908 0.3582 0.1849 0.6774 0.7143 0.0656 0.9091 0.5538 0.6136 0.6667 0.2278 0.0 0.4412 0.5 0.6667 0.8805 0.6167 0.5846 0.3478
0.0148 5.9194 4700 3.2196 0.6152 0.5491 0.9271 0.9062 0.8261 0.5 0.1467 0.6571 0.6026 0.08 0.9062 0.5588 0.7429 0.7812 0.1591 0.0 0.3667 0.6667 0.8 0.9452 0.9231 0.5116 0.3810 0.7807 0.928 0.3918 0.4595 0.1774 0.7188 0.6861 0.1176 0.8657 0.6129 0.6753 0.7143 0.2 0.0 0.3607 0.5437 0.7619 0.8790 0.6102 0.6377 0.4103
0.021 6.0453 4800 3.2310 0.6113 0.5423 0.9583 0.9062 0.8261 0.5294 0.1467 0.5429 0.6026 0.06 0.9375 0.5294 0.7429 0.75 0.2045 0.0 0.3667 0.6667 0.6667 0.9452 0.9744 0.4651 0.3810 0.736 0.9206 0.4 0.4235 0.1964 0.6441 0.6963 0.0952 0.8955 0.5455 0.6582 0.8 0.24 0.0 0.3667 0.5490 0.7018 0.8679 0.6726 0.5970 0.3810
0.0377 6.1713 4900 3.2332 0.6240 0.5569 0.9271 0.9141 0.8261 0.5 0.1333 0.6286 0.6410 0.06 0.9688 0.5882 0.7429 0.8438 0.1818 0.0 0.3667 0.7143 0.7 0.9315 0.9744 0.5581 0.3810 0.7639 0.9323 0.4130 0.4096 0.1754 0.7097 0.7092 0.0984 0.9254 0.6061 0.6933 0.7941 0.2192 0.0 0.3729 0.5405 0.75 0.8718 0.6441 0.6667 0.4
0.0004 6.2972 5000 3.2382 0.6182 0.5485 0.9479 0.9141 0.8696 0.4118 0.1333 0.6571 0.6154 0.08 0.9375 0.5588 0.7143 0.7188 0.2273 0.0 0.4 0.6429 0.6333 0.9589 1.0 0.5581 0.3810 0.7745 0.9323 0.4124 0.3544 0.1802 0.6970 0.7111 0.1290 0.8824 0.5846 0.6410 0.7797 0.2439 0.0 0.4138 0.5347 0.7037 0.8805 0.65 0.6667 0.3478
0.0099 6.4232 5100 3.2434 0.6133 0.5455 0.9479 0.8828 0.8696 0.4706 0.1467 0.6286 0.6154 0.04 0.9062 0.5882 0.7429 0.7812 0.1818 0.0 0.4333 0.6667 0.7333 0.9589 0.9231 0.4651 0.3810 0.7552 0.9150 0.4124 0.4156 0.1880 0.6567 0.7111 0.0678 0.8788 0.6061 0.6420 0.7812 0.2078 0.0 0.4194 0.5437 0.7719 0.8805 0.6486 0.6061 0.3478
0.0009 6.5491 5200 3.2188 0.6113 0.5396 0.9167 0.8984 0.8696 0.4412 0.12 0.5429 0.6410 0.06 0.9375 0.5882 0.7143 0.75 0.1364 0.0 0.4 0.6905 0.6667 0.9589 0.9744 0.5814 0.3810 0.7928 0.92 0.4040 0.3947 0.1607 0.6230 0.7246 0.0938 0.8824 0.5882 0.6098 0.7742 0.1714 0.0 0.3934 0.5321 0.7407 0.8642 0.6230 0.6667 0.3721
0.0066 6.6751 5300 3.2749 0.6113 0.5482 0.9583 0.8516 0.8696 0.4706 0.1333 0.6857 0.6026 0.06 0.9062 0.5588 0.7143 0.75 0.1591 0.0 0.4333 0.6667 0.6333 0.9726 0.9231 0.6047 0.3810 0.7731 0.8934 0.4124 0.4103 0.1653 0.7164 0.6912 0.0968 0.8788 0.6032 0.6757 0.7869 0.1867 0.0 0.4062 0.5385 0.6909 0.8659 0.6372 0.7123 0.3721
0.0006 6.8010 5400 3.2724 0.6006 0.5389 0.9375 0.8203 0.8696 0.4412 0.1467 0.6857 0.6154 0.04 0.9062 0.5588 0.7143 0.7812 0.1818 0.0 0.3667 0.6429 0.6 0.9589 0.9231 0.5581 0.3810 0.7407 0.875 0.4082 0.375 0.176 0.6957 0.7111 0.0645 0.8657 0.5758 0.6757 0.7937 0.2078 0.0 0.3860 0.5243 0.6792 0.875 0.6486 0.6761 0.3636
0.0006 6.9270 5500 3.2286 0.6201 0.5505 0.9479 0.8906 0.8261 0.5294 0.1333 0.6286 0.6538 0.06 0.9688 0.5882 0.7429 0.7812 0.1136 0.0 0.4333 0.6667 0.6333 0.9589 0.9487 0.5814 0.3810 0.7845 0.9194 0.4086 0.4337 0.1739 0.6875 0.7338 0.0984 0.9118 0.5797 0.6842 0.7937 0.1429 0.0 0.4262 0.5185 0.7037 0.8642 0.6379 0.6849 0.3721

Framework versions

  • Transformers 4.52.2
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1
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