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segformer-b0-scene-parse-150

This model is a fine-tuned version of nvidia/segformer-b0-finetuned-ade-512-512 on the scene_parse_150 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7301
  • Mean Iou: 0.2965
  • Mean Accuracy: 0.4377
  • Overall Accuracy: 0.7652
  • Per Category Iou: [0.8373553548834576, 0.1843522646293565, 0.8561534001899617, 0.5935064239350731, 0.743349831530489, 0.8681947852454764, 0.9250299082812707, 0.8556302781654894, 0.8640881331577057, 0.771978889737043, 0.03785841157066734, 0.0, 0.0, 0.03589606810250939, 0.5981697514324581, 0.29679105909439757, 0.9310517713357466, 0.22213488306366141, nan, 0.07176005924463096, nan, nan, 0.7325212037855716, 0.0, 0.0005248, 0.3258772749937671, nan, 0.0, 0.0016232302281540265, nan, 0.5927057165061177, nan, nan, 0.6416097190584662, nan, nan, 0.34275618374558303, nan, nan, 0.0, nan, 0.04019661659763461, nan, 0.33401103688643624, nan, nan, nan, nan, nan, 0.020353508302088912, nan, nan, nan, nan, nan, nan, 0.9573868044210392, nan, nan, nan, 0.7211958680969408, nan, 0.5146986960211929, nan, nan, nan, 0.0, 0.27069954128440366, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.648936170212766, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.015436182411572727, nan, nan, nan, 0.0, nan, nan, nan, 0.1557234166182452, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan]
  • Per Category Accuracy: [0.9100444036135354, 0.827616992783275, 0.9531705977271947, 0.9469571782671871, 0.8938780435826399, 0.9574093629003871, 0.9250299082812707, 0.9659623572480204, 0.8640881331577057, 0.9564703388448619, 0.3796437659033079, nan, nan, 0.07027850304612707, 0.9150009458351724, 0.4532634971796938, 0.9403204245321235, 0.2614639318869356, nan, 0.07259333250093644, nan, nan, 0.780332100746529, nan, 0.0014741838055515606, 0.3475042371473198, nan, 0.0, 0.0018720099840532484, nan, 0.9987209550202516, nan, nan, 0.9454545454545454, nan, nan, 0.3737957610789981, nan, nan, 0.0, nan, 0.040838572297708375, nan, 0.33401103688643624, nan, nan, nan, nan, nan, 0.020353508302088912, nan, nan, nan, nan, nan, nan, 0.9980685245620879, nan, nan, nan, 0.9553947368421053, nan, 0.8398522746348833, nan, nan, nan, 0.0, 0.2876378480472796, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7429963459196103, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.015436182411572727, nan, nan, nan, 0.0, nan, nan, nan, 0.15930255597384585, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, 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
0.5264 1.0 20 0.5047 0.3682 0.5065 0.8426 [0.8565739071653898, 0.6058302019727572, 0.8046102810218482, 0.6527634958184112, 0.8374741225546262, 0.9325371345404735, 0.9200105596620908, 0.8611270377212914, 0.9252784097712848, 0.8497935596476947, 0.18255625144977963, 0.0, 0.0, 0.05921306607275427, 0.5735577447380857, 0.32586774227788984, 0.9213701854066042, 0.6803915319063276, nan, 0.5313837028393621, nan, nan, 0.8109935725763256, 0.0, 0.10490891296509332, 0.7135816885138868, nan, 0.0, 0.29652660421007726, nan, 0.6798810703666998, nan, nan, 0.05508377323846683, 0.0, nan, 0.2896890343698854, nan, nan, 0.0, nan, 0.5532536520584329, nan, 0.3409090909090909, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.9213011266494235, nan, nan, nan, 0.7026341463414634, nan, 0.39556036450320375, nan, 0.0, nan, 0.0, 0.23496185873173003, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6337741607324516, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.5328362944162437, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.022451612903225806, nan, nan, nan, 0.0, nan, nan, nan, 0.7066388040507957, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan] [0.8954866141361318, 0.7676884160404732, 0.8127373227589522, 0.9178609077181686, 0.900551266217677, 0.9585093277015135, 0.926492090921175, 0.9681588476383749, 0.9252784097712848, 0.9447624172455402, 0.40050890585241733, nan, nan, 0.08677110530896431, 0.9948924900687307, 0.44978389861548607, 0.9858995652056496, 0.8432942105974447, nan, 0.5458609064802098, nan, nan, 0.845112677039001, nan, 0.21968934272975693, 0.9443438237346715, nan, 0.0, 0.3132670040906885, nan, 0.9609891281176721, nan, nan, 0.06713286713286713, nan, nan, 0.34104046242774566, nan, nan, 0.0, nan, 0.580840600283918, nan, 0.3409090909090909, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.9997906775387484, nan, nan, nan, 0.9476315789473684, nan, 0.9819120362598623, nan, nan, nan, 0.0, 0.2458417108389691, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.758830694275274, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.5328362944162437, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.023021963482402753, nan, nan, nan, 0.0, nan, nan, nan, 0.871012482662968, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan]
0.4616 2.0 40 0.5074 0.3776 0.5336 0.8470 [0.8610993283349392, 0.628927536231884, 0.8794637424016295, 0.7075239062273351, 0.8193332727500473, 0.934080322836843, 0.926492090921175, 0.8280969205417026, 0.9149802418871992, 0.8676857119051046, 0.2088445807770961, 0.0, 0.0, 0.00012180638874508969, 0.6348064959198514, 0.39189836333417843, 0.9502164213446802, 0.6948582224720504, nan, 0.22333935282346626, nan, nan, 0.8152351159772372, 0.0, 0.07089538717976591, 0.711711203818544, nan, 0.0, 0.44540144047263436, nan, 0.5617035301010693, nan, 0.0, 0.6105141980046047, 0.0, nan, 0.38109860116001365, nan, nan, 0.0, nan, 0.6690308988764045, nan, 0.5728289282602381, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.903455153500288, nan, nan, nan, 0.6314034037458308, nan, 0.39643018369969096, nan, 0.0, nan, 0.0, 0.37806918137175544, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6388059701492538, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.30076142131979694, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.11746498491062447, nan, nan, nan, 0.0, nan, nan, nan, 0.7114791362234129, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan] [0.9026035593559709, 0.8071572055650621, 0.9427747450818827, 0.9032878429584889, 0.8565398352106853, 0.9573213657162971, 0.926492090921175, 0.971803748358601, 0.9149802418871992, 0.9339513420082836, 0.4157760814249364, nan, nan, 0.00017406440382941688, 0.9908569266662463, 0.5095597392132445, 0.9619116098193308, 0.9252544363935006, nan, 0.2285428892495942, nan, nan, 0.8545663852647736, nan, 0.14069466417373797, 0.9439616496626898, nan, 0.0, 0.46521181446301046, nan, 0.9342509973505497, nan, nan, 0.8900699300699301, nan, nan, 0.4304431599229287, nan, nan, 0.0, nan, 0.7245234232407219, nan, 0.5728289282602381, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.9998858241120446, nan, nan, nan, 0.9714473684210526, nan, 0.958200436461306, nan, nan, nan, 0.0, 0.4268567598854566, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.781973203410475, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.30076142131979694, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.1338978565758137, nan, nan, nan, 0.0, nan, nan, nan, 0.8682385575589459, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan]
0.4723 3.0 60 0.5240 0.3447 0.5248 0.8347 [0.8317879374467388, 0.6561169367651731, 0.8736286371616354, 0.6743377207597467, 0.7999474992210749, 0.9280968050100838, 0.9237006513359032, 0.8669088060392408, 0.9531641043975688, 0.8466990370189714, 0.18634015687775016, 0.0, 0.0, 0.036465367547752266, 0.6138525101340817, 0.3679081489778773, 0.9437882458142313, 0.651796696538394, nan, 0.2152591170825336, nan, nan, 0.7207213224244449, 0.0, 0.07335497375845645, 0.6980257943213168, nan, 0.0, 0.368204092444209, nan, 0.5304595043586977, nan, 0.0, 0.5693491718253304, 0.0, nan, 0.3264448336252189, nan, nan, 0.0, nan, 0.6381575826573692, nan, 0.41170490851002034, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.9138973203802433, nan, nan, nan, 0.5751076260762608, nan, 0.4037211110325818, nan, 0.0, nan, 0.018691588785046728, 0.3836802754020763, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.6069699903194579, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.23651649746192893, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.13813178089441652, nan, nan, nan, 0.0, nan, nan, nan, 0.6673376950176145, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan] [0.867972863149712, 0.7680976117848375, 0.9464895114498575, 0.9225049146699336, 0.8372185576698411, 0.9617652235128475, 0.9237006513359032, 0.9687398034300266, 0.9577296132199736, 0.9340736392394743, 0.4956743002544529, nan, nan, 0.049347258485639686, 0.9930638754019799, 0.48121016775327813, 0.9548563288233547, 0.9254831432640629, nan, 0.22404794606068174, nan, nan, 0.7528779739063699, nan, 0.1738817776499353, 0.9438287195506962, nan, 0.0, 0.38551272273452125, nan, 0.9432347656606876, nan, nan, 0.9518881118881118, nan, nan, 0.35915221579961465, nan, nan, 0.0, nan, 0.6820624619752585, nan, 0.41170490851002034, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.9997906775387484, nan, nan, nan, 0.9843421052631579, nan, 0.9589138828269264, nan, nan, nan, 0.019801980198019802, 0.4345945287272284, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7637028014616322, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.23651649746192893, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.13813178089441652, nan, nan, nan, 0.0, nan, nan, nan, 0.7881910045571626, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan]
0.3251 4.0 80 0.5120 0.3559 0.5235 0.8368 [0.8401795741478856, 0.6016992860826841, 0.8888679979253112, 0.6580747844104884, 0.8204388118733854, 0.9246785444154971, 0.9250299082812707, 0.8036625048285615, 0.9039635971739911, 0.8490021393195205, 0.12653269754768393, 0.0, 0.0, 0.3545617282077733, 0.6103429419576596, 0.2835154642163541, 0.9533147773279352, 0.6778089759045337, nan, 0.10561400106656324, nan, nan, 0.8208471211118464, 0.0, 0.07048420117905331, 0.7432988945885413, nan, 0.0, 0.31081528578051526, nan, 0.5386598818346368, nan, 0.0, 0.5806755991696546, 0.0, nan, 0.3804092586380409, nan, nan, 0.0, nan, 0.29632385771692216, nan, 0.5731919837351147, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.9300787819775161, nan, nan, nan, 0.6081324807345466, nan, 0.42961785247856454, nan, 0.0, nan, 0.14728682170542637, 0.35242707637497217, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6181998021760633, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.09819162436548223, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.06333245126576696, nan, nan, nan, 0.0, nan, nan, nan, 0.6846528828374516, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan] [0.8745450690788311, 0.8245666245071052, 0.941387715865005, 0.9365224080458133, 0.8553741395291514, 0.9682110172474481, 0.9250299082812707, 0.9768731845131511, 0.9039635971739911, 0.9448031829892705, 0.378117048346056, nan, nan, 0.5085291557876415, 0.9853080269878303, 0.3582521427001685, 0.9665734964487294, 0.923507947563752, nan, 0.10880259707828693, nan, nan, 0.8653457057140864, nan, 0.20548684021285776, 0.9402146821308697, nan, 0.0, 0.3253830687096998, nan, 0.9606541401467856, nan, nan, 0.8606993006993007, nan, nan, 0.4369942196531792, nan, nan, 0.0, nan, 0.3028290407625228, nan, 0.5731919837351147, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.9997145602801115, nan, nan, nan, 0.9760526315789474, nan, 0.9525768004028874, nan, nan, nan, 0.18811881188118812, 0.38573082312800827, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.761266747868453, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.09819162436548223, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.06333245126576696, nan, nan, nan, 0.0, nan, nan, nan, 0.8070140677630275, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan]
0.3222 5.0 100 0.5179 0.3578 0.5108 0.8358 [0.8572340336193263, 0.6093136851348756, 0.905848778087584, 0.6520875444343766, 0.8106645736500656, 0.9238028587182423, 0.9260933138375648, 0.8389827603934606, 0.892707460184409, 0.820210028225137, 0.11164725457570715, 0.0, 0.0, 0.1613608952963285, 0.6185902524210192, 0.29350311997063555, 0.9603155384089725, 0.6558235991861808, nan, 0.09146612640213471, nan, nan, 0.8129767178496486, 0.0, 0.06567240177949295, 0.740152723882312, nan, 0.0, 0.2646927659032268, nan, 0.5020897545546967, nan, 0.0, 0.5867009551800147, nan, nan, 0.29547410084322834, nan, nan, 0.0, nan, 0.35968233674272226, nan, 0.5981702004066222, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.9471571846026191, nan, nan, nan, 0.672455200443377, nan, 0.4290154269245983, nan, 0.0, nan, 0.1322314049586777, 0.3069078757849859, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6272321428571429, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.17338197969543148, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.01967010673017553, nan, nan, nan, 0.0, nan, nan, nan, 0.6590130226182317, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan] [0.901717253807286, 0.825980209805818, 0.9480825351048855, 0.9329111940511126, 0.8486264561567476, 0.9583113340373108, 0.9260933138375648, 0.9740559468385659, 0.892707460184409, 0.966661774777419, 0.3414758269720102, nan, nan, 0.25034812880765883, 0.9827857998612775, 0.3807413376309428, 0.9772408112560426, 0.8679113864834239, nan, 0.09244599825196653, nan, nan, 0.851461661899114, nan, 0.1762188983172731, 0.936542487787046, nan, 0.0, 0.2880642030090827, nan, 0.9475287023784146, nan, nan, 0.8934265734265734, nan, nan, 0.3308285163776493, nan, nan, 0.0, nan, 0.37084262827012776, nan, 0.5981702004066222, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.9992007687843122, nan, nan, nan, 0.9578947368421052, nan, 0.9558502601980863, nan, nan, nan, 0.15841584158415842, 0.324559800158411, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.6845310596833131, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.17338197969543148, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.01967010673017553, nan, nan, nan, 0.0, nan, nan, nan, 0.762036853576382, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan]
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0.4808 50.0 1000 0.7301 0.2965 0.4377 0.7652 [0.8373553548834576, 0.1843522646293565, 0.8561534001899617, 0.5935064239350731, 0.743349831530489, 0.8681947852454764, 0.9250299082812707, 0.8556302781654894, 0.8640881331577057, 0.771978889737043, 0.03785841157066734, 0.0, 0.0, 0.03589606810250939, 0.5981697514324581, 0.29679105909439757, 0.9310517713357466, 0.22213488306366141, nan, 0.07176005924463096, nan, nan, 0.7325212037855716, 0.0, 0.0005248, 0.3258772749937671, nan, 0.0, 0.0016232302281540265, nan, 0.5927057165061177, nan, nan, 0.6416097190584662, nan, nan, 0.34275618374558303, nan, nan, 0.0, nan, 0.04019661659763461, nan, 0.33401103688643624, nan, nan, nan, nan, nan, 0.020353508302088912, nan, nan, nan, nan, nan, nan, 0.9573868044210392, nan, nan, nan, 0.7211958680969408, nan, 0.5146986960211929, nan, nan, nan, 0.0, 0.27069954128440366, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.648936170212766, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.015436182411572727, nan, nan, nan, 0.0, nan, nan, nan, 0.1557234166182452, nan, nan, 0.0, nan, nan, nan, 0.0, nan, 0.0, nan] [0.9100444036135354, 0.827616992783275, 0.9531705977271947, 0.9469571782671871, 0.8938780435826399, 0.9574093629003871, 0.9250299082812707, 0.9659623572480204, 0.8640881331577057, 0.9564703388448619, 0.3796437659033079, nan, nan, 0.07027850304612707, 0.9150009458351724, 0.4532634971796938, 0.9403204245321235, 0.2614639318869356, nan, 0.07259333250093644, nan, nan, 0.780332100746529, nan, 0.0014741838055515606, 0.3475042371473198, nan, 0.0, 0.0018720099840532484, nan, 0.9987209550202516, nan, nan, 0.9454545454545454, nan, nan, 0.3737957610789981, nan, nan, 0.0, nan, 0.040838572297708375, nan, 0.33401103688643624, nan, nan, nan, nan, nan, 0.020353508302088912, nan, nan, nan, nan, nan, nan, 0.9980685245620879, nan, nan, nan, 0.9553947368421053, nan, 0.8398522746348833, nan, nan, nan, 0.0, 0.2876378480472796, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.7429963459196103, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.015436182411572727, nan, nan, nan, 0.0, nan, nan, nan, 0.15930255597384585, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan]

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.13.3
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Dataset used to train nommis/nommis_model

Evaluation results

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