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segformer-b0-scene-parse-150

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metadata
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
  - generated_from_trainer
datasets:
  - scene_parse_150
model-index:
  - name: segformer-b0-scene-parse-150
    results: []

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: 2.4256
  • Mean Iou: 0.1067
  • Mean Accuracy: 0.1610
  • Overall Accuracy: 0.6058
  • Per Category Iou: [0.15751233214579335, 0.4954199940633185, 0.7847812312141546, 0.8873635118116774, 0.3508498692001278, 0.8037955555333535, 0.31617090045957275, 0.36421611430408396, nan, 0.008443710307892765, 0.46832122794886594, 0.0, 0.0, 0.36932538827865125, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.009200044878267699, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
  • Per Category Accuracy: [0.33305808470414394, 0.8896136677961007, 0.9590632244101719, 0.986865394145912, 0.9040082164015154, 0.9294017414910976, 0.6900903880070547, 0.7177221908332906, nan, 0.008510770312613106, 0.5343069500586778, 0.0, 0.0, 0.6045281836342983, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.009206759108516252, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 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: 4
  • eval_batch_size: 4
  • 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
4.676 2.0 20 4.8074 0.0149 0.0561 0.2707 [0.03078593412115176, 0.3580055097857451, 0.2493979510810846, 0.6510134220839453, 0.10116613390003036, 0.18134161648031505, 0.0, 0.02355489933626792, 0.0, 0.0, 0.0, 0.0, 0.0, 0.004951078627843923, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0045448852148084525, 0.0, 0.011462999784394988, 0.0, 0.0, 0.0, 0.0005617011520605261, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.088454038365607, nan, 0.0, 0.0, 0.0, 0.03900981302750071, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0002709048221058335, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, 0.0] [0.0497041077236214, 0.5150782972548412, 0.3804087224569015, 0.9700123435128837, 0.11559415873706835, 0.18192661477812594, 0.0, 0.029225948944475832, nan, 0.0, 0.0, 0.0, 0.0, 0.006580751302440362, nan, 0.0, 0.0, nan, 0.0, 0.005694684214066319, 0.0, 0.16117442317408634, nan, 0.0, 0.0, 0.0029116406203577158, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.12901412177133975, nan, nan, 0.0, nan, 0.08891752577319588, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.000289110183103116, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
4.1453 4.0 40 4.1874 0.0338 0.0881 0.4289 [0.10980330661353455, 0.42174392644358427, 0.31296040965727584, 0.8674240075068962, 0.22176803414817528, 0.3663374241525617, 0.0, 0.09713242961418143, 0.0, 0.0, 0.0, 0.0, 0.0, 0.15511911256592106, 0.0, 0.0, 0.0, 0.0, 0.0013457656445256176, 0.02729680625177856, 0.0, 0.001669449081803005, 0.0, 0.0, 0.0, 0.0027932305236720418, nan, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.004981716042185595, nan, 0.0, 0.0, nan, 0.012686382733338825, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0] [0.2586685209732212, 0.8995484611754098, 0.9137582709957957, 0.9902267697242256, 0.316977615747196, 0.3710444614518202, 0.0, 0.15948430838769326, nan, 0.0, 0.0, 0.0, 0.0, 0.16706490657683418, nan, 0.0, 0.0, nan, 0.0038033143167617496, 0.027957760688742908, 0.0, 0.0035367428282714873, nan, 0.0, 0.0, 0.007071127220868738, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.006217548037173, nan, nan, 0.0, nan, 0.014175257731958763, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
3.8845 6.0 60 3.6624 0.0487 0.1005 0.4695 [0.1357764641350383, 0.4445678020166201, 0.3563719539417566, 0.8437178556696725, 0.2658015641433192, 0.5154016592809783, 0.0, 0.064553881592391, nan, 0.0, 0.019440919001594587, 0.0, 0.0, 0.2293191474169017, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03006585219839915, 0.0, 0.0021514629948364886, nan, 0.0, 0.0, 0.01335995085995086, nan, nan, 0.0, 0.0, nan, 0.0, nan, nan, 6.557162060260319e-05, nan, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan] [0.48230803218960283, 0.8819588754499539, 0.9311465970405596, 0.9947947929460363, 0.4572481856580263, 0.5360933416123955, 0.0, 0.1189925865050422, nan, 0.0, 0.01947450775850828, 0.0, 0.0, 0.25159622390222885, nan, 0.0, 0.0, nan, 0.0, 0.030401542497141446, 0.0, 0.0025262448773367766, nan, 0.0, 0.0, 0.015508942896191098, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 6.614412805503192e-05, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
3.445 8.0 80 3.4450 0.0679 0.1135 0.5196 [0.1428938825414225, 0.4457272524382604, 0.5367781455317888, 0.8256990373075254, 0.32042652556693296, 0.6885072523481232, 0.0, 0.160865633533104, nan, 0.0, 0.038344089608540015, 0.0, 0.0, 0.26781635828009664, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0020847810979847115, 0.0, 0.004646078163432632, nan, 0.0, 0.0, 0.030647291941875824, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] [0.4910000941640047, 0.9238418604432735, 0.9169301637570301, 0.9953365531125045, 0.5404052427171259, 0.7576930297035422, 0.0, 0.3106648175732361, nan, 0.0, 0.03896857478158821, 0.0, 0.0, 0.3169336832621724, nan, 0.0, 0.0, nan, 0.0, 0.002085061542945542, 0.0, 0.004771795879413911, nan, 0.0, 0.0, 0.03446431754709133, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
3.7725 10.0 100 3.2800 0.0719 0.1127 0.5316 [0.14866169293363043, 0.4544055397100093, 0.5470708728299151, 0.8822002587378853, 0.3350510262915129, 0.7244125888069177, 0.0, 0.15745654045303284, nan, 0.0, 0.06796049277022731, 0.0, 0.0, 0.12659045159330015, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.009410822445812685, nan, 0.0, 0.0, 0.0001768033946251768, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.4774549642538951, 0.9303695667998036, 0.9399325910740257, 0.9907716080734578, 0.6324044537249274, 0.7974469682748263, 0.0, 0.31473111099696954, nan, 0.0, 0.07038727343851871, 0.0, 0.0, 0.1305574053037722, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.010554089709762533, nan, 0.0, 0.0, 0.0001782637114504724, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
3.2718 12.0 120 3.1059 0.0747 0.1168 0.5387 [0.14967188348602747, 0.49213370499515563, 0.5717284560184414, 0.8753823563737824, 0.3027330829328566, 0.7264728455284553, 0.0, 0.20754074114226198, nan, 0.0, 0.07052131516439555, 0.0, 0.0, 0.11101500507690572, nan, 0.0, 0.0, nan, 0.0, 0.002354101742035289, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.4571227826187734, 0.8921378707936799, 0.9368897581121529, 0.9927908959666571, 0.7532426899192644, 0.8064518923914457, 0.0, 0.45887479240501994, nan, 0.0, 0.07355587429912636, 0.0, 0.0, 0.11563320145716637, nan, 0.0, 0.0, nan, 0.0, 0.002354101742035289, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
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2.367 46.0 460 2.4341 0.1085 0.1585 0.6044 [0.14435276526949917, 0.5001397612982507, 0.7745585120782178, 0.8887973755457179, 0.35364684323794504, 0.8084452742288774, 0.3641478319914638, 0.31961286734399985, nan, 0.003129680318132521, 0.5545558673615486, 0.0, 0.0, 0.3716152450090744, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.015120967741935484, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.36153907444026745, 0.89068909567786, 0.9657941893307257, 0.9832454512154205, 0.9030967943791107, 0.9241686040634792, 0.6207010582010583, 0.5509870392247505, nan, 0.0031333698720799165, 0.631314382579215, 0.0, 0.0, 0.6015511770927181, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.01515746926402066, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
2.3357 48.0 480 2.4640 0.1073 0.1575 0.6016 [0.14214285132615087, 0.4993168607712234, 0.7911497071019549, 0.8879407663191446, 0.3589272190298686, 0.8056112124191099, 0.3337951737674633, 0.31418920572757314, nan, 0.004694301696772926, 0.5220583170703708, 0.0, 0.0, 0.3757431919943236, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.006677890011223345, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.37929985440796193, 0.8907384056899058, 0.9546404443628169, 0.9878380998993435, 0.8671228311216612, 0.9301237527255924, 0.6373456790123457, 0.5494290067971305, nan, 0.0046948842307732086, 0.5929912635284913, 0.0, 0.0, 0.6015511770927181, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.006680514231179476, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]
2.1061 50.0 500 2.4256 0.1067 0.1610 0.6058 [0.15751233214579335, 0.4954199940633185, 0.7847812312141546, 0.8873635118116774, 0.3508498692001278, 0.8037955555333535, 0.31617090045957275, 0.36421611430408396, nan, 0.008443710307892765, 0.46832122794886594, 0.0, 0.0, 0.36932538827865125, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.009200044878267699, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan] [0.33305808470414394, 0.8896136677961007, 0.9590632244101719, 0.986865394145912, 0.9040082164015154, 0.9294017414910976, 0.6900903880070547, 0.7177221908332906, nan, 0.008510770312613106, 0.5343069500586778, 0.0, 0.0, 0.6045281836342983, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.009206759108516252, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan]

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.0
  • Datasets 3.0.1
  • Tokenizers 0.20.0