segformer-b5-finetuned-segments-instryde-foot-test

This model is a fine-tuned version of nvidia/mit-b5 on the inStryde/inStrydeSegmentationFoot dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0496
  • Mean Iou: 0.4672
  • Mean Accuracy: 0.9344
  • Overall Accuracy: 0.9344
  • Per Category Iou: [0.0, 0.9343870058298716]
  • Per Category Accuracy: [nan, 0.9343870058298716]

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: 6e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
0.1392 0.23 20 0.2371 0.4064 0.8128 0.8128 [0.0, 0.8127920708469037] [nan, 0.8127920708469037]
0.2273 0.45 40 0.0993 0.4449 0.8898 0.8898 [0.0, 0.889800913515142] [nan, 0.889800913515142]
0.0287 0.68 60 0.0607 0.4190 0.8379 0.8379 [0.0, 0.8379005425233161] [nan, 0.8379005425233161]
0.03 0.91 80 0.0572 0.4072 0.8144 0.8144 [0.0, 0.8144304164916533] [nan, 0.8144304164916533]
0.0239 1.14 100 0.0577 0.3973 0.7946 0.7946 [0.0, 0.7946284254068925] [nan, 0.7946284254068925]
0.0196 1.36 120 0.0425 0.4227 0.8455 0.8455 [0.0, 0.8454754171184029] [nan, 0.8454754171184029]
0.0295 1.59 140 0.0368 0.4479 0.8958 0.8958 [0.0, 0.895802316554768] [nan, 0.895802316554768]
0.0297 1.82 160 0.0441 0.4561 0.9121 0.9121 [0.0, 0.9121241975954804] [nan, 0.9121241975954804]
0.0276 2.05 180 0.0332 0.4629 0.9258 0.9258 [0.0, 0.925774145806165] [nan, 0.925774145806165]
0.0148 2.27 200 0.0395 0.4310 0.8621 0.8621 [0.0, 0.8620666905637888] [nan, 0.8620666905637888]
0.012 2.5 220 0.0372 0.4381 0.8761 0.8761 [0.0, 0.8761025846276997] [nan, 0.8761025846276997]
0.0117 2.73 240 0.0339 0.4471 0.8941 0.8941 [0.0, 0.8941320836457919] [nan, 0.8941320836457919]
0.0198 2.95 260 0.0297 0.4485 0.8969 0.8969 [0.0, 0.8969491585060927] [nan, 0.8969491585060927]
0.0247 3.18 280 0.0303 0.4565 0.9130 0.9130 [0.0, 0.9130423308930413] [nan, 0.9130423308930413]
0.0115 3.41 300 0.0307 0.4533 0.9066 0.9066 [0.0, 0.9065626188900153] [nan, 0.9065626188900153]
0.0164 3.64 320 0.0330 0.4549 0.9097 0.9097 [0.0, 0.9097436483868343] [nan, 0.9097436483868343]
0.0114 3.86 340 0.0362 0.4425 0.8850 0.8850 [0.0, 0.8849727418868903] [nan, 0.8849727418868903]
0.012 4.09 360 0.0321 0.4582 0.9164 0.9164 [0.0, 0.9164498699219532] [nan, 0.9164498699219532]
0.0153 4.32 380 0.0321 0.4572 0.9144 0.9144 [0.0, 0.9144310762281544] [nan, 0.9144310762281544]
0.0115 4.55 400 0.0307 0.4573 0.9145 0.9145 [0.0, 0.9145300367033407] [nan, 0.9145300367033407]
0.0139 4.77 420 0.0330 0.4678 0.9357 0.9357 [0.0, 0.935664695520609] [nan, 0.935664695520609]
0.014 5.0 440 0.0317 0.4635 0.9271 0.9271 [0.0, 0.9270562337402442] [nan, 0.9270562337402442]
0.0197 5.23 460 0.0320 0.4678 0.9356 0.9356 [0.0, 0.9355745315321061] [nan, 0.9355745315321061]
0.0086 5.45 480 0.0337 0.4607 0.9214 0.9214 [0.0, 0.9213528116870122] [nan, 0.9213528116870122]
0.3103 5.68 500 0.0338 0.4548 0.9096 0.9096 [0.0, 0.9095853116265363] [nan, 0.9095853116265363]
0.0088 5.91 520 0.0305 0.4635 0.9270 0.9270 [0.0, 0.9270243464760175] [nan, 0.9270243464760175]
0.0119 6.14 540 0.0299 0.4680 0.9359 0.9359 [0.0, 0.9359494817769782] [nan, 0.9359494817769782]
0.0114 6.36 560 0.0314 0.4574 0.9148 0.9148 [0.0, 0.914796130425508] [nan, 0.914796130425508]
0.0122 6.59 580 0.0289 0.4613 0.9227 0.9227 [0.0, 0.9226920767845322] [nan, 0.9226920767845322]
0.0164 6.82 600 0.0312 0.4620 0.9240 0.9240 [0.0, 0.9239807620836238] [nan, 0.9239807620836238]
0.0062 7.05 620 0.0335 0.4605 0.9210 0.9210 [0.0, 0.9209954544155065] [nan, 0.9209954544155065]
0.0089 7.27 640 0.0309 0.4659 0.9317 0.9317 [0.0, 0.9317029778306545] [nan, 0.9317029778306545]
0.0251 7.5 660 0.0291 0.4734 0.9468 0.9468 [0.0, 0.9467878529315391] [nan, 0.9467878529315391]
0.0065 7.73 680 0.0326 0.4598 0.9195 0.9195 [0.0, 0.9195297398219151] [nan, 0.9195297398219151]
0.0056 7.95 700 0.0310 0.4606 0.9213 0.9213 [0.0, 0.9212714441851925] [nan, 0.9212714441851925]
0.0099 8.18 720 0.0345 0.4503 0.9006 0.9006 [0.0, 0.9006183930138303] [nan, 0.9006183930138303]
0.0103 8.41 740 0.0335 0.4539 0.9078 0.9078 [0.0, 0.9077512441530853] [nan, 0.9077512441530853]
0.0065 8.64 760 0.0334 0.4544 0.9088 0.9088 [0.0, 0.9087936278250467] [nan, 0.9087936278250467]
0.0047 8.86 780 0.0341 0.4557 0.9114 0.9114 [0.0, 0.9114215782216583] [nan, 0.9114215782216583]
0.0105 9.09 800 0.0315 0.4597 0.9195 0.9195 [0.0, 0.9194703635368034] [nan, 0.9194703635368034]
0.0087 9.32 820 0.0329 0.4583 0.9166 0.9166 [0.0, 0.9165708216138474] [nan, 0.9165708216138474]
0.0122 9.55 840 0.0357 0.4537 0.9073 0.9073 [0.0, 0.9073004242105703] [nan, 0.9073004242105703]
0.0057 9.77 860 0.0319 0.4621 0.9241 0.9241 [0.0, 0.9241050124580242] [nan, 0.9241050124580242]
0.0068 10.0 880 0.0342 0.4539 0.9078 0.9078 [0.0, 0.907799624829843] [nan, 0.907799624829843]
0.0095 10.23 900 0.0340 0.4578 0.9156 0.9156 [0.0, 0.9155933120311748] [nan, 0.9155933120311748]
0.0043 10.45 920 0.0319 0.4636 0.9272 0.9272 [0.0, 0.9271771854321385] [nan, 0.9271771854321385]
0.0049 10.68 940 0.0308 0.4659 0.9319 0.9319 [0.0, 0.9318525181042692] [nan, 0.9318525181042692]
0.005 10.91 960 0.0319 0.4640 0.9281 0.9281 [0.0, 0.9280612323438019] [nan, 0.9280612323438019]
0.0043 11.14 980 0.0313 0.4653 0.9306 0.9306 [0.0, 0.930638602941985] [nan, 0.930638602941985]
0.0084 11.36 1000 0.0321 0.4632 0.9264 0.9264 [0.0, 0.9264294840640648] [nan, 0.9264294840640648]
0.0044 11.59 1020 0.0320 0.4643 0.9285 0.9285 [0.0, 0.9285241474555063] [nan, 0.9285241474555063]
0.0044 11.82 1040 0.0321 0.4661 0.9321 0.9321 [0.0, 0.9321098153397533] [nan, 0.9321098153397533]
0.0057 12.05 1060 0.0338 0.4626 0.9253 0.9253 [0.0, 0.9252518544093489] [nan, 0.9252518544093489]
0.0064 12.27 1080 0.0348 0.4616 0.9231 0.9231 [0.0, 0.9231450958487181] [nan, 0.9231450958487181]
0.0075 12.5 1100 0.0331 0.4618 0.9237 0.9237 [0.0, 0.9236706859280404] [nan, 0.9236706859280404]
0.0103 12.73 1120 0.0317 0.4704 0.9408 0.9408 [0.0, 0.9408425274945187] [nan, 0.9408425274945187]
0.0053 12.95 1140 0.0320 0.4704 0.9407 0.9407 [0.0, 0.9407292727284723] [nan, 0.9407292727284723]
0.0073 13.18 1160 0.0331 0.4652 0.9305 0.9305 [0.0, 0.9304681710124976] [nan, 0.9304681710124976]
0.0052 13.41 1180 0.0342 0.4664 0.9328 0.9328 [0.0, 0.9328047377877275] [nan, 0.9328047377877275]
0.0089 13.64 1200 0.0322 0.4676 0.9353 0.9353 [0.0, 0.9352996413232555] [nan, 0.9352996413232555]
0.0054 13.86 1220 0.0332 0.4655 0.9311 0.9311 [0.0, 0.9310509382552609] [nan, 0.9310509382552609]
0.0057 14.09 1240 0.0333 0.4661 0.9321 0.9321 [0.0, 0.9321439017256508] [nan, 0.9321439017256508]
0.0047 14.32 1260 0.0346 0.4639 0.9278 0.9278 [0.0, 0.9277522557490538] [nan, 0.9277522557490538]
0.0092 14.55 1280 0.0380 0.4583 0.9166 0.9166 [0.0, 0.9166290983381238] [nan, 0.9166290983381238]
0.0066 14.77 1300 0.0338 0.4638 0.9277 0.9277 [0.0, 0.927687381659765] [nan, 0.927687381659765]
0.0076 15.0 1320 0.0347 0.4640 0.9280 0.9280 [0.0, 0.9279897608895007] [nan, 0.9279897608895007]
0.0054 15.23 1340 0.0345 0.4647 0.9295 0.9295 [0.0, 0.9294664710914461] [nan, 0.9294664710914461]
0.0036 15.45 1360 0.0349 0.4666 0.9332 0.9332 [0.0, 0.9331950818842955] [nan, 0.9331950818842955]
0.004 15.68 1380 0.0352 0.4617 0.9234 0.9234 [0.0, 0.9234408777134413] [nan, 0.9234408777134413]
0.0042 15.91 1400 0.0357 0.4622 0.9244 0.9244 [0.0, 0.9244282833436326] [nan, 0.9244282833436326]
0.0048 16.14 1420 0.0370 0.4586 0.9172 0.9172 [0.0, 0.9171546884174461] [nan, 0.9171546884174461]
0.0043 16.36 1440 0.0345 0.4647 0.9294 0.9294 [0.0, 0.9294411811922318] [nan, 0.9294411811922318]
0.0027 16.59 1460 0.0354 0.4667 0.9334 0.9334 [0.0, 0.9333754098613014] [nan, 0.9333754098613014]
0.0057 16.82 1480 0.0364 0.4689 0.9379 0.9379 [0.0, 0.9378913062122988] [nan, 0.9378913062122988]
0.0035 17.05 1500 0.0363 0.4662 0.9325 0.9325 [0.0, 0.9324682721720945] [nan, 0.9324682721720945]
0.0029 17.27 1520 0.0348 0.4674 0.9347 0.9347 [0.0, 0.9347212723238338] [nan, 0.9347212723238338]
0.0043 17.5 1540 0.0362 0.4648 0.9295 0.9295 [0.0, 0.9295390421065827] [nan, 0.9295390421065827]
0.0041 17.73 1560 0.0347 0.4664 0.9328 0.9328 [0.0, 0.9328487202211436] [nan, 0.9328487202211436]
0.003 17.95 1580 0.0364 0.4649 0.9297 0.9297 [0.0, 0.9297237683269303] [nan, 0.9297237683269303]
0.0121 18.18 1600 0.0364 0.4650 0.9300 0.9300 [0.0, 0.9299920611707684] [nan, 0.9299920611707684]
0.004 18.41 1620 0.0369 0.4667 0.9334 0.9334 [0.0, 0.9334259896597299] [nan, 0.9334259896597299]
0.0035 18.64 1640 0.0368 0.4636 0.9272 0.9272 [0.0, 0.9272475573256042] [nan, 0.9272475573256042]
0.0031 18.86 1660 0.0358 0.4665 0.9330 0.9330 [0.0, 0.9329784683997212] [nan, 0.9329784683997212]
0.0032 19.09 1680 0.0357 0.4661 0.9322 0.9322 [0.0, 0.9321515986514985] [nan, 0.9321515986514985]
0.0047 19.32 1700 0.0371 0.4621 0.9243 0.9243 [0.0, 0.9242886391175364] [nan, 0.9242886391175364]
0.0056 19.55 1720 0.0359 0.4663 0.9326 0.9326 [0.0, 0.9326277084932278] [nan, 0.9326277084932278]
0.0033 19.77 1740 0.0348 0.4694 0.9389 0.9389 [0.0, 0.9388523223824404] [nan, 0.9388523223824404]
0.0049 20.0 1760 0.0394 0.4612 0.9224 0.9224 [0.0, 0.9223918966764674] [nan, 0.9223918966764674]
0.0058 20.23 1780 0.0368 0.4660 0.9321 0.9321 [0.0, 0.9320724302713497] [nan, 0.9320724302713497]
0.003 20.45 1800 0.0370 0.4686 0.9372 0.9372 [0.0, 0.9371787907909581] [nan, 0.9371787907909581]
0.0058 20.68 1820 0.0363 0.4665 0.9330 0.9330 [0.0, 0.9329949618122522] [nan, 0.9329949618122522]
0.0083 20.91 1840 0.0351 0.4661 0.9322 0.9322 [0.0, 0.9321834859157253] [nan, 0.9321834859157253]
0.0036 21.14 1860 0.0353 0.4667 0.9333 0.9333 [0.0, 0.9333149340153543] [nan, 0.9333149340153543]
0.0032 21.36 1880 0.0373 0.4657 0.9314 0.9314 [0.0, 0.93137640826254] [nan, 0.93137640826254]
0.005 21.59 1900 0.0391 0.4647 0.9294 0.9294 [0.0, 0.929370809298766] [nan, 0.929370809298766]
0.0049 21.82 1920 0.0364 0.4701 0.9403 0.9403 [0.0, 0.9402795523467927] [nan, 0.9402795523467927]
0.0044 22.05 1940 0.0368 0.4672 0.9343 0.9343 [0.0, 0.9343111361322288] [nan, 0.9343111361322288]
0.0038 22.27 1960 0.0367 0.4663 0.9325 0.9325 [0.0, 0.932513354166346] [nan, 0.932513354166346]
0.0032 22.5 1980 0.0378 0.4679 0.9358 0.9358 [0.0, 0.9358483221801213] [nan, 0.9358483221801213]
0.0039 22.73 2000 0.0381 0.4653 0.9306 0.9306 [0.0, 0.9305517376359882] [nan, 0.9305517376359882]
0.0032 22.95 2020 0.0385 0.4651 0.9301 0.9301 [0.0, 0.9301262075926875] [nan, 0.9301262075926875]
0.0058 23.18 2040 0.0381 0.4654 0.9309 0.9309 [0.0, 0.9308673115957486] [nan, 0.9308673115957486]
0.0049 23.41 2060 0.0377 0.4658 0.9316 0.9316 [0.0, 0.9316194112071639] [nan, 0.9316194112071639]
0.0032 23.64 2080 0.0373 0.4692 0.9384 0.9384 [0.0, 0.9384256927783043] [nan, 0.9384256927783043]
0.0056 23.86 2100 0.0390 0.4646 0.9292 0.9292 [0.0, 0.9292465589243656] [nan, 0.9292465589243656]
0.003 24.09 2120 0.0383 0.4658 0.9317 0.9317 [0.0, 0.9316765883706047] [nan, 0.9316765883706047]
0.0037 24.32 2140 0.0376 0.4668 0.9337 0.9337 [0.0, 0.9336755899693663] [nan, 0.9336755899693663]
0.0025 24.55 2160 0.0390 0.4663 0.9326 0.9326 [0.0, 0.9326145137632029] [nan, 0.9326145137632029]
0.0039 24.77 2180 0.0381 0.4688 0.9376 0.9376 [0.0, 0.937613117320942] [nan, 0.937613117320942]
0.0031 25.0 2200 0.0395 0.4645 0.9291 0.9291 [0.0, 0.9290629322648534] [nan, 0.9290629322648534]
0.0026 25.23 2220 0.0389 0.4668 0.9336 0.9336 [0.0, 0.9335678330074968] [nan, 0.9335678330074968]
0.0028 25.45 2240 0.0375 0.4680 0.9359 0.9359 [0.0, 0.9359329883644473] [nan, 0.9359329883644473]
0.0039 25.68 2260 0.0404 0.4656 0.9312 0.9312 [0.0, 0.9312004785288756] [nan, 0.9312004785288756]
0.004 25.91 2280 0.0371 0.4716 0.9431 0.9431 [0.0, 0.9431021250112706] [nan, 0.9431021250112706]
0.0048 26.14 2300 0.0373 0.4700 0.9400 0.9400 [0.0, 0.9399639783870323] [nan, 0.9399639783870323]
0.0033 26.36 2320 0.0385 0.4688 0.9377 0.9377 [0.0, 0.9376560001935227] [nan, 0.9376560001935227]
0.0042 26.59 2340 0.0374 0.4686 0.9372 0.9372 [0.0, 0.9371743925476165] [nan, 0.9371743925476165]
0.0048 26.82 2360 0.0393 0.4660 0.9320 0.9320 [0.0, 0.9319789676003404] [nan, 0.9319789676003404]
0.0047 27.05 2380 0.0393 0.4650 0.9300 0.9300 [0.0, 0.9300162515091472] [nan, 0.9300162515091472]
0.0048 27.27 2400 0.0389 0.4670 0.9340 0.9340 [0.0, 0.9339867656857851] [nan, 0.9339867656857851]
0.004 27.5 2420 0.0388 0.4673 0.9346 0.9346 [0.0, 0.9345750307327253] [nan, 0.9345750307327253]
0.0051 27.73 2440 0.0386 0.4655 0.9309 0.9309 [0.0, 0.9309002984208107] [nan, 0.9309002984208107]
0.0045 27.95 2460 0.0395 0.4664 0.9328 0.9328 [0.0, 0.932816832956917] [nan, 0.932816832956917]
0.0042 28.18 2480 0.0393 0.4642 0.9285 0.9285 [0.0, 0.9284856628262672] [nan, 0.9284856628262672]
0.0035 28.41 2500 0.0396 0.4667 0.9333 0.9333 [0.0, 0.9333083366503419] [nan, 0.9333083366503419]
0.0036 28.64 2520 0.0395 0.4664 0.9327 0.9327 [0.0, 0.9327288680900848] [nan, 0.9327288680900848]
0.0035 28.86 2540 0.0377 0.4675 0.9349 0.9349 [0.0, 0.9349378858084081] [nan, 0.9349378858084081]
0.0029 29.09 2560 0.0402 0.4658 0.9315 0.9315 [0.0, 0.9315479397528627] [nan, 0.9315479397528627]
0.0042 29.32 2580 0.0398 0.4691 0.9383 0.9383 [0.0, 0.9382893472347145] [nan, 0.9382893472347145]
0.0029 29.55 2600 0.0405 0.4668 0.9336 0.9336 [0.0, 0.9336129150017483] [nan, 0.9336129150017483]
0.0023 29.77 2620 0.0402 0.4666 0.9332 0.9332 [0.0, 0.9332071770534849] [nan, 0.9332071770534849]
0.0036 30.0 2640 0.0417 0.4648 0.9296 0.9296 [0.0, 0.9296435003859459] [nan, 0.9296435003859459]
0.0045 30.23 2660 0.0395 0.4674 0.9348 0.9348 [0.0, 0.9347960424606412] [nan, 0.9347960424606412]
0.0025 30.45 2680 0.0400 0.4695 0.9390 0.9390 [0.0, 0.9390392477244589] [nan, 0.9390392477244589]
0.0032 30.68 2700 0.0404 0.4673 0.9347 0.9347 [0.0, 0.9346926837421135] [nan, 0.9346926837421135]
0.0047 30.91 2720 0.0416 0.4651 0.9303 0.9303 [0.0, 0.9302790465488084] [nan, 0.9302790465488084]
0.0024 31.14 2740 0.0403 0.4677 0.9355 0.9355 [0.0, 0.9354997613952987] [nan, 0.9354997613952987]
0.0037 31.36 2760 0.0406 0.4677 0.9354 0.9354 [0.0, 0.9354469824751994] [nan, 0.9354469824751994]
0.0031 31.59 2780 0.0414 0.4671 0.9343 0.9343 [0.0, 0.9342858462330146] [nan, 0.9342858462330146]
0.0036 31.82 2800 0.0404 0.4670 0.9339 0.9339 [0.0, 0.9339152942314839] [nan, 0.9339152942314839]
0.003 32.05 2820 0.0411 0.4678 0.9355 0.9355 [0.0, 0.9355151552469944] [nan, 0.9355151552469944]
0.0038 32.27 2840 0.0423 0.4672 0.9344 0.9344 [0.0, 0.9344221917766045] [nan, 0.9344221917766045]
0.0023 32.5 2860 0.0433 0.4657 0.9313 0.9313 [0.0, 0.9313401227549717] [nan, 0.9313401227549717]
0.003 32.73 2880 0.0421 0.4682 0.9363 0.9363 [0.0, 0.9363365271910399] [nan, 0.9363365271910399]
0.0031 32.95 2900 0.0428 0.4679 0.9357 0.9357 [0.0, 0.9357086779540251] [nan, 0.9357086779540251]
0.0026 33.18 2920 0.0448 0.4656 0.9311 0.9311 [0.0, 0.9311081154187018] [nan, 0.9311081154187018]
0.0031 33.41 2940 0.0456 0.4639 0.9279 0.9279 [0.0, 0.9278929995359854] [nan, 0.9278929995359854]
0.0022 33.64 2960 0.0424 0.4674 0.9349 0.9349 [0.0, 0.9348851068883088] [nan, 0.9348851068883088]
0.0025 33.86 2980 0.0434 0.4654 0.9308 0.9308 [0.0, 0.9307782471680811] [nan, 0.9307782471680811]
0.0025 34.09 3000 0.0418 0.4675 0.9351 0.9351 [0.0, 0.9350610366219732] [nan, 0.9350610366219732]
0.003 34.32 3020 0.0424 0.4674 0.9349 0.9349 [0.0, 0.9348653147932716] [nan, 0.9348653147932716]
0.0021 34.55 3040 0.0412 0.4687 0.9374 0.9374 [0.0, 0.9374437849522901] [nan, 0.9374437849522901]
0.0043 34.77 3060 0.0412 0.4676 0.9352 0.9352 [0.0, 0.9352446632814854] [nan, 0.9352446632814854]
0.005 35.0 3080 0.0428 0.4675 0.9350 0.9350 [0.0, 0.9349807686809888] [nan, 0.9349807686809888]
0.003 35.23 3100 0.0430 0.4672 0.9344 0.9344 [0.0, 0.934393603194884] [nan, 0.934393603194884]
0.0027 35.45 3120 0.0452 0.4652 0.9303 0.9303 [0.0, 0.9303428210772617] [nan, 0.9303428210772617]
0.0022 35.68 3140 0.0441 0.4653 0.9306 0.9306 [0.0, 0.9305847244610502] [nan, 0.9305847244610502]
0.0029 35.91 3160 0.0425 0.4671 0.9342 0.9342 [0.0, 0.9341692927844619] [nan, 0.9341692927844619]
0.0022 36.14 3180 0.0438 0.4679 0.9358 0.9358 [0.0, 0.9358153353550592] [nan, 0.9358153353550592]
0.0028 36.36 3200 0.0443 0.4680 0.9359 0.9359 [0.0, 0.935929689681941] [nan, 0.935929689681941]
0.0025 36.59 3220 0.0433 0.4682 0.9365 0.9365 [0.0, 0.9364948639513379] [nan, 0.9364948639513379]
0.003 36.82 3240 0.0439 0.4680 0.9359 0.9359 [0.0, 0.9359340879252827] [nan, 0.9359340879252827]
0.0027 37.05 3260 0.0462 0.4665 0.9331 0.9331 [0.0, 0.9330587363407056] [nan, 0.9330587363407056]
0.004 37.27 3280 0.0447 0.4675 0.9350 0.9350 [0.0, 0.9349917642893428] [nan, 0.9349917642893428]
0.0032 37.5 3300 0.0442 0.4683 0.9367 0.9367 [0.0, 0.9366916853408749] [nan, 0.9366916853408749]
0.0019 37.73 3320 0.0454 0.4674 0.9347 0.9347 [0.0, 0.9347102767154798] [nan, 0.9347102767154798]
0.0028 37.95 3340 0.0451 0.4674 0.9349 0.9349 [0.0, 0.9348543191849176] [nan, 0.9348543191849176]
0.0023 38.18 3360 0.0457 0.4669 0.9337 0.9337 [0.0, 0.9337228710852885] [nan, 0.9337228710852885]
0.0028 38.41 3380 0.0454 0.4675 0.9351 0.9351 [0.0, 0.9350764304736688] [nan, 0.9350764304736688]
0.0024 38.64 3400 0.0467 0.4677 0.9354 0.9354 [0.0, 0.9353568184866964] [nan, 0.9353568184866964]
0.0023 38.86 3420 0.0463 0.4669 0.9337 0.9337 [0.0, 0.9337096763552637] [nan, 0.9337096763552637]
0.0029 39.09 3440 0.0456 0.4664 0.9328 0.9328 [0.0, 0.9328289281261064] [nan, 0.9328289281261064]
0.0026 39.32 3460 0.0453 0.4686 0.9372 0.9372 [0.0, 0.9371578991350854] [nan, 0.9371578991350854]
0.0037 39.55 3480 0.0458 0.4678 0.9356 0.9356 [0.0, 0.9356097174788389] [nan, 0.9356097174788389]
0.0025 39.77 3500 0.0468 0.4671 0.9342 0.9342 [0.0, 0.9342275695087382] [nan, 0.9342275695087382]
0.0048 40.0 3520 0.0459 0.4668 0.9335 0.9335 [0.0, 0.933527149256587] [nan, 0.933527149256587]
0.0027 40.23 3540 0.0468 0.4658 0.9315 0.9315 [0.0, 0.9315490393136981] [nan, 0.9315490393136981]
0.0019 40.45 3560 0.0465 0.4662 0.9324 0.9324 [0.0, 0.9323792077444268] [nan, 0.9323792077444268]
0.0033 40.68 3580 0.0459 0.4674 0.9348 0.9348 [0.0, 0.9348015402648182] [nan, 0.9348015402648182]
0.004 40.91 3600 0.0467 0.4667 0.9333 0.9333 [0.0, 0.9333358256712269] [nan, 0.9333358256712269]
0.0022 41.14 3620 0.0469 0.4665 0.9331 0.9331 [0.0, 0.9330521389756931] [nan, 0.9330521389756931]
0.0036 41.36 3640 0.0458 0.4676 0.9352 0.9352 [0.0, 0.9352479619639916] [nan, 0.9352479619639916]
0.0024 41.59 3660 0.0468 0.4671 0.9342 0.9342 [0.0, 0.9341769897103097] [nan, 0.9341769897103097]
0.0021 41.82 3680 0.0466 0.4658 0.9317 0.9317 [0.0, 0.9316776879314402] [nan, 0.9316776879314402]
0.0032 42.05 3700 0.0472 0.4666 0.9332 0.9332 [0.0, 0.9331807875934351] [nan, 0.9331807875934351]
0.0023 42.27 3720 0.0470 0.4673 0.9347 0.9347 [0.0, 0.9346827876945948] [nan, 0.9346827876945948]
0.003 42.5 3740 0.0474 0.4661 0.9321 0.9321 [0.0, 0.9321482999689924] [nan, 0.9321482999689924]
0.0025 42.73 3760 0.0483 0.4656 0.9313 0.9313 [0.0, 0.9312851447132016] [nan, 0.9312851447132016]
0.0019 42.95 3780 0.0471 0.4669 0.9338 0.9338 [0.0, 0.9338130350737915] [nan, 0.9338130350737915]
0.0032 43.18 3800 0.0463 0.4682 0.9365 0.9365 [0.0, 0.9364508815179218] [nan, 0.9364508815179218]
0.0026 43.41 3820 0.0484 0.4657 0.9315 0.9315 [0.0, 0.9314698709335492] [nan, 0.9314698709335492]
0.0019 43.64 3840 0.0477 0.4673 0.9345 0.9345 [0.0, 0.9345486412726757] [nan, 0.9345486412726757]
0.003 43.86 3860 0.0472 0.4688 0.9375 0.9375 [0.0, 0.9375218537716036] [nan, 0.9375218537716036]
0.0025 44.09 3880 0.0473 0.4670 0.9340 0.9340 [0.0, 0.9339999604158099] [nan, 0.9339999604158099]
0.0019 44.32 3900 0.0481 0.4670 0.9340 0.9340 [0.0, 0.9340263498758595] [nan, 0.9340263498758595]
0.0024 44.55 3920 0.0478 0.4671 0.9343 0.9343 [0.0, 0.9342561580904587] [nan, 0.9342561580904587]
0.0021 44.77 3940 0.0479 0.4677 0.9355 0.9355 [0.0, 0.9354579780835535] [nan, 0.9354579780835535]
0.0019 45.0 3960 0.0479 0.4682 0.9363 0.9363 [0.0, 0.9363112372918256] [nan, 0.9363112372918256]
0.0024 45.23 3980 0.0481 0.4681 0.9362 0.9362 [0.0, 0.9362133763774748] [nan, 0.9362133763774748]
0.0023 45.45 4000 0.0497 0.4670 0.9340 0.9340 [0.0, 0.933970272273254] [nan, 0.933970272273254]
0.0027 45.68 4020 0.0487 0.4671 0.9343 0.9343 [0.0, 0.9342781493071667] [nan, 0.9342781493071667]
0.0023 45.91 4040 0.0477 0.4672 0.9344 0.9344 [0.0, 0.9344309882632876] [nan, 0.9344309882632876]
0.003 46.14 4060 0.0485 0.4678 0.9356 0.9356 [0.0, 0.9355877262621309] [nan, 0.9355877262621309]
0.0017 46.36 4080 0.0488 0.4677 0.9354 0.9354 [0.0, 0.9353678140950504] [nan, 0.9353678140950504]
0.0022 46.59 4100 0.0481 0.4668 0.9337 0.9337 [0.0, 0.9336634948001769] [nan, 0.9336634948001769]
0.0032 46.82 4120 0.0487 0.4676 0.9352 0.9352 [0.0, 0.935249061524827] [nan, 0.935249061524827]
0.0021 47.05 4140 0.0483 0.4675 0.9351 0.9351 [0.0, 0.9350885256428583] [nan, 0.9350885256428583]
0.002 47.27 4160 0.0486 0.4673 0.9347 0.9347 [0.0, 0.9346530995520389] [nan, 0.9346530995520389]
0.0028 47.5 4180 0.0487 0.4675 0.9349 0.9349 [0.0, 0.9349224919567125] [nan, 0.9349224919567125]
0.0026 47.73 4200 0.0482 0.4667 0.9335 0.9335 [0.0, 0.9334589764847919] [nan, 0.9334589764847919]
0.0022 47.95 4220 0.0490 0.4670 0.9341 0.9341 [0.0, 0.9340769296742881] [nan, 0.9340769296742881]
0.0027 48.18 4240 0.0489 0.4679 0.9358 0.9358 [0.0, 0.9358153353550592] [nan, 0.9358153353550592]
0.0021 48.41 4260 0.0491 0.4676 0.9353 0.9353 [0.0, 0.9352864465932307] [nan, 0.9352864465932307]
0.0024 48.64 4280 0.0491 0.4672 0.9344 0.9344 [0.0, 0.9343804084648591] [nan, 0.9343804084648591]
0.0025 48.86 4300 0.0493 0.4675 0.9349 0.9349 [0.0, 0.9349466822950914] [nan, 0.9349466822950914]
0.0022 49.09 4320 0.0484 0.4677 0.9354 0.9354 [0.0, 0.9353623162908734] [nan, 0.9353623162908734]
0.0027 49.32 4340 0.0480 0.4677 0.9354 0.9354 [0.0, 0.9354117965284665] [nan, 0.9354117965284665]
0.0018 49.55 4360 0.0498 0.4675 0.9350 0.9350 [0.0, 0.9349983616543552] [nan, 0.9349983616543552]
0.0021 49.77 4380 0.0493 0.4672 0.9345 0.9345 [0.0, 0.9344738711358683] [nan, 0.9344738711358683]
0.0017 50.0 4400 0.0496 0.4672 0.9344 0.9344 [0.0, 0.9343870058298716] [nan, 0.9343870058298716]

Framework versions

  • Transformers 4.21.1
  • Pytorch 1.12.0+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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