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segformer-b0-scene-parse-150

Image Segmentation
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segformer
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segformer-b0-scene-parse-150

This model is a fine-tuned version of nvidia/mit-b0 on the scene_parse_150 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.6339
  • Mean Iou: 0.1200
  • Mean Accuracy: 0.1669
  • Overall Accuracy: 0.6123
  • Per Category Iou: [0.5349505300503856, nan, nan, 0.7662725216601061, nan, 0.5326853699336921, nan, nan, 0.34424006183640854, nan, 0.4690602972950636, nan, 0.0, nan, 0.43864150176543804, nan, nan, 0.0, 0.03116323751411952, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11546494517491812, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.21709821831740012, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2656179069036561, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004383351344800073, nan, nan, nan, nan, nan, nan, 0.27573695030755746, 0.0, 0.0, nan, nan, 0.1730424387328153, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2699536864879483, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
  • Per Category Accuracy: [0.7970510497742438, nan, nan, 0.9333683660650987, nan, 0.5709001366216121, nan, nan, 0.5022173025701339, nan, 0.5997946735914005, nan, 0.0, nan, 0.6798811830944017, nan, nan, 0.0, 0.03494731857464189, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18674548490489992, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.4055014699503591, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.399427626224171, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005731113077237973, nan, nan, nan, nan, nan, nan, 0.3817023254759232, 0.0, 0.0, nan, nan, 0.18656355727404544, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.49311473385204396, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]

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
3.6823 1.0 20 3.5003 0.0586 0.1033 0.4973 [0.49515664733932946, nan, nan, 0.5775935923663784, nan, 0.04596206382865156, nan, nan, 0.26871056425122297, nan, 0.11369973679410002, nan, 0.0, 0.0, 0.3107990029966113, nan, nan, 0.07862248213125406, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.034315093061257025, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.01068727200680629, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.11023414367115379, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.000786938713592233, nan, nan, nan, nan, nan, nan, 0.05070892559181233, 0.0, 0.0, nan, nan, 0.015040183696900114, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.11313313426108514, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] [0.7426263023684041, nan, nan, 0.9727086727777018, nan, 0.05114940356296204, nan, nan, 0.47199142135667094, nan, 0.13174104716468385, nan, 0.0, nan, 0.4854913009051258, nan, nan, 0.08009119658748254, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.08262480087518953, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.011048725239770591, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.5815895187334261, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0010960423626975848, nan, nan, nan, nan, nan, nan, 0.06443529825277504, 0.0, 0.0, nan, nan, 0.021105203802158853, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.12399654192910954, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
3.4276 2.0 40 3.1882 0.0710 0.1169 0.4890 [0.43876531073668484, nan, nan, 0.5793460668074677, nan, 0.30489642184557436, nan, nan, 0.32595816708983194, nan, 0.14653798821167174, nan, 0.0, nan, 0.2911432340397004, nan, nan, 0.060054857802800635, 0.002022419470540954, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.024744866592893152, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.07467207433837308, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.12499059372413274, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004522511609125783, nan, nan, nan, nan, nan, nan, 0.09628943874020461, 0.0, 0.0, nan, nan, 0.029982363315696647, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.12235712170565591, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] [0.5900893611665498, nan, nan, 0.9783242745736993, nan, 0.3846789392114827, nan, nan, 0.559548929399149, nan, 0.17078325985868711, nan, 0.0, nan, 0.6459378704825733, nan, nan, 0.06118996837537692, 0.002770214277258198, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.06180066406924745, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.10514964576606102, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.47971748890697613, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0059159876926327465, nan, nan, nan, nan, nan, nan, 0.11675000283456353, 0.0, 0.0, nan, nan, 0.0356049621395199, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.1275781153513647, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
2.9493 3.0 60 3.0877 0.0780 0.1289 0.5137 [0.45940221778653784, nan, nan, 0.6494007938527302, nan, 0.4439837201805287, nan, nan, 0.34639082849373676, nan, 0.22427847777966967, nan, 0.000308090455357693, nan, 0.34777956821568423, nan, nan, 0.004698282190574071, 0.0031407729433002986, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.03480888089115643, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0913573016991002, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14217375469266544, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.001518325086206108, nan, nan, nan, nan, nan, nan, 0.09802372235537879, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.04033963860518523, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] [0.582026866520922, nan, nan, 0.952329573015261, nan, 0.5949736206887242, nan, nan, 0.6362931958905531, nan, 0.3181804456790869, nan, 0.000308090455357693, nan, 0.7024633946812373, nan, nan, 0.0047069206442597634, 0.0040369361903634425, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.13068345392779687, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13909104053207383, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.5329640034657495, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0021920847253951697, nan, nan, nan, nan, nan, nan, 0.12659160742428882, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.041218969988884774, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
3.0162 4.0 80 2.8616 0.0814 0.1232 0.5420 [0.4910465857166263, nan, nan, 0.6443584008757899, nan, 0.45141247019724007, nan, nan, 0.32257705190056235, nan, 0.26924304549583833, nan, 0.0, nan, 0.3331696608404026, nan, nan, 0.0, 0.001900597974698759, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.020747307143488526, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13337046977599973, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.13325950535252862, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0017460871773707102, nan, nan, nan, nan, nan, nan, 0.1319458915051747, 0.0, 0.0, nan, nan, 0.009900990099009901, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06578507371941024, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] [0.7132203547582256, nan, nan, 0.8931325481026355, nan, 0.5735731681633843, nan, nan, 0.581002455982566, nan, 0.33111902892686756, nan, 0.0, nan, 0.611482717739854, nan, nan, 0.0, 0.002995146205753522, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.03153369287756943, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.19039471781772616, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.37913198729225195, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0021788793957241144, nan, nan, nan, nan, nan, nan, 0.1723074480991417, 0.0, 0.0, nan, nan, 0.01031093926212341, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06681486970482894, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
3.0263 5.0 100 2.7084 0.0843 0.1324 0.5396 [0.49507208751747933, nan, nan, 0.6203053779437833, nan, 0.4485318235941677, nan, nan, 0.3407940967092522, nan, 0.2978191928406968, nan, 0.0, nan, 0.3557274457037466, nan, nan, 0.0, 0.0022259234469125043, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.026368862501178023, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.12652470809007507, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14366688649397702, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.006174898362074405, nan, nan, nan, nan, nan, nan, 0.19123126092560874, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06350128408951938, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] [0.6526707344030444, nan, nan, 0.9749474509023519, nan, 0.5390505607966174, nan, nan, 0.6160157736345083, nan, 0.414004468868893, nan, 0.0, nan, 0.639577053813209, nan, nan, 0.0, 0.003385817449982242, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.05370132238066906, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.23396308255819556, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.45186021477144434, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.009045650824672838, nan, nan, nan, nan, nan, nan, 0.2455865845777067, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06412868963813759, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
3.0192 6.0 120 2.5853 0.0834 0.1228 0.5584 [0.513016303842118, nan, nan, 0.6309294285637532, nan, 0.35611384488558007, nan, nan, 0.2789754627300978, nan, 0.33300786268564675, nan, 0.0, nan, 0.39549797351503974, nan, nan, 0.0, 0.0006107421807796855, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.009196650667414078, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.13424914500097224, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.12323683312478671, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004757876929583422, nan, nan, nan, nan, nan, nan, 0.24288385441579335, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06377284193687319, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] [0.7580542978289542, nan, nan, 0.9493663014141616, nan, 0.37790185924193903, nan, nan, 0.49498772008717007, nan, 0.3913279787426777, nan, 0.0, nan, 0.6642459982413698, nan, nan, 0.0, 0.0008405351012193679, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.01007619522868165, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.2828329076100053, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.22755795940872214, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005942398351974857, nan, nan, nan, nan, nan, nan, 0.31665476149982424, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.06400518710633568, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
2.7891 7.0 140 2.4388 0.0948 0.1331 0.5748 [0.5154656376508916, nan, nan, 0.6863149964423734, nan, 0.49124725952151366, nan, nan, 0.31201516142142743, nan, 0.35972157545158207, nan, 0.0, nan, 0.39671683424856585, nan, nan, 0.0, 2.370192459627722e-05, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.009904517600114009, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.163059324189356, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.14525411857851944, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.3573847356082313, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07118873794781752, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] [0.7711226677412429, nan, nan, 0.9177684357159737, nan, 0.5529665250049951, nan, nan, 0.4886298384586115, nan, 0.44317289691406486, nan, 0.0, nan, 0.594793229711226, nan, nan, 0.0, 3.551556765715639e-05, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.02134234113198856, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.31670200973540896, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.25047916611967336, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.49779470957062033, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.07135358774854884, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]
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1.0631 50.0 1000 1.6339 0.1200 0.1669 0.6123 [0.5349505300503856, nan, nan, 0.7662725216601061, nan, 0.5326853699336921, nan, nan, 0.34424006183640854, nan, 0.4690602972950636, nan, 0.0, nan, 0.43864150176543804, nan, nan, 0.0, 0.03116323751411952, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.11546494517491812, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.21709821831740012, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.2656179069036561, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.004383351344800073, nan, nan, nan, nan, nan, nan, 0.27573695030755746, 0.0, 0.0, nan, nan, 0.1730424387328153, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.2699536864879483, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan] [0.7970510497742438, nan, nan, 0.9333683660650987, nan, 0.5709001366216121, nan, nan, 0.5022173025701339, nan, 0.5997946735914005, nan, 0.0, nan, 0.6798811830944017, nan, nan, 0.0, 0.03494731857464189, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.18674548490489992, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.4055014699503591, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.399427626224171, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.005731113077237973, nan, nan, nan, nan, nan, nan, 0.3817023254759232, 0.0, 0.0, nan, nan, 0.18656355727404544, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.49311473385204396, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan]

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1
  • Datasets 2.14.5
  • Tokenizers 0.13.3
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