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segformer-b0-scene-parse-150

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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: 4.0103
  • Mean Iou: 0.0037
  • Mean Accuracy: 0.0189
  • Overall Accuracy: 0.0739
  • Per Category Iou: [0.11679271033436381, 0.03128502961519977, 0.023624261128838208, 0.0, 0.0, 0.0, 0.01419157101429142, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
  • Per Category Accuracy: [0.29724773650855246, 0.6037630897966093, 0.0256234471444005, 0.0, 0.0, 0.0, 0.018120842572062083, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]

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
5.0493 1.0 20 4.9144 0.0010 0.0163 0.0364 [0.0017021322583782733, 0.0, 0.00667942392802728, 0.0, 0.0, 0.05155631938241674, 0.0, 5.319219670474342e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01693611642057007, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.006038917468127935, 0.0, 0.0, 0.0, 0.0, 0.0010416028429630537, 0.0, nan, 0.0, 8.555783709787816e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.004764322414248289, nan, 0.0, 0.0, nan, nan, 0.00011429834153106438, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] [0.001721701916008275, 0.0, 0.00696435790085837, 0.0, 0.0, 0.6614231634949868, 0.0, 5.8913632614587014e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.032105323940408825, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.01646909061977882, nan, 0.0, nan, 0.0, 0.0010421775380088278, nan, nan, nan, 9.004466215242761e-05, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.07377822504560035, nan, nan, nan, nan, nan, 0.019280205655526992, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
4.8287 2.0 40 4.7077 0.0020 0.0202 0.0524 [0.002801803559428584, 0.0, 0.0355611761503973, 0.0, 3.697039903384024e-05, 0.05095927977565143, 0.0010170690722561245, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.022151088348271446, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004784210855037162, 0.0, 0.0, 0.0, 0.0, 0.015589596607084068, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.005461689587426326, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] [0.002825777430400883, 0.0, 0.0410816739990213, 0.0, 3.702149714934472e-05, 0.8339898369824705, 0.0010199556541019956, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.06243503868807022, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.010157265229267775, nan, 0.0, nan, 0.0, 0.024675085826385484, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.034970752877539465, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
4.52 3.0 60 4.5629 0.0021 0.0210 0.0514 [0.0023208658050961326, 0.0, 0.016972349696430408, 0.0, 0.0007119359748612952, 0.052525455364613505, 0.0042261221365698974, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.023618004449768953, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.006555759199210489, nan, 0.0, nan, 0.0, 0.012853045841529152, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, 0.006843426527734791, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] [0.002336595457439802, 0.0, 0.018351768630130185, 0.0, 0.0007157489448873312, 0.8697479708069027, 0.004284922394678492, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.10159949185818223, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.014798313310525897, nan, 0.0, nan, 0.0, 0.026238352133398726, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.012516510472356752, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
4.712 4.0 80 4.2786 0.0025 0.0209 0.0586 [0.001796099199103039, 0.0, 0.045937848675556836, 0.00011612513644703533, 0.0, 0.05119292264770398, 0.003078831251509231, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.019878260096858755, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.001135918527222875, nan, 0.0, nan, 0.0, 0.009498458415519256, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] [0.0018036877215324787, 0.0, 0.05440756859750692, 0.00011613772385546273, 0.0, 0.9389877907373303, 0.0031097560975609758, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.032292989952650425, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0015381759355026917, nan, 0.0, nan, 0.0, 0.015203531142717018, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
4.3219 5.0 100 4.1609 0.0041 0.0215 0.0727 [0.01996516250986151, 0.0, 0.09470965633816977, 0.0007417263106380376, 0.0, 0.05010706615483478, 0.012319069940364046, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.022247101489092806, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0022439605904421302, nan, 0.0, nan, 0.0, 0.016400817470212464, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] [0.020955571891986435, 0.0, 0.1425211164721485, 0.000751023947598659, 0.0, 0.8084117727303731, 0.013592017738359202, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.046757708742349004, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0033945951680059406, nan, 0.0, nan, 0.0, 0.039112309955860715, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
4.3819 6.0 120 3.9211 0.0046 0.0211 0.0810 [0.02846564456574089, 0.0, 0.10832047814662474, 0.00030011542901115814, 0.0, 0.0495111013431391, 0.02222758021445111, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.009556941383433977, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.015524033143810761, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] [0.031012497369622073, 0.0, 0.19305408133594634, 0.0003019580820242031, 0.0, 0.7644430802810177, 0.027084257206208426, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.013959464141355815, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.024521824423737126, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
4.0513 7.0 140 3.8826 0.0059 0.0212 0.0976 [0.08633022645395634, 0.001970813195063582, 0.11362945301678162, 4.6443223159687285e-05, 0.0, 0.048898879809053226, 0.04347258698595501, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0055388492030475555, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] [0.12691675981165126, 0.002031488065007618, 0.2226230523556125, 4.645508954218509e-05, 0.0, 0.6274213900825318, 0.07386363636363637, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.006727104746506525, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]
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3.2996 50.0 1000 4.0103 0.0037 0.0189 0.0739 [0.11679271033436381, 0.03128502961519977, 0.023624261128838208, 0.0, 0.0, 0.0, 0.01419157101429142, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0] [0.29724773650855246, 0.6037630897966093, 0.0256234471444005, 0.0, 0.0, 0.0, 0.018120842572062083, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 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, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0]

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0
  • Datasets 2.17.1
  • Tokenizers 0.15.2
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