Instructions to use JabaleNurAdnan/CS_Checkpoints_Dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use JabaleNurAdnan/CS_Checkpoints_Dataset with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://JabaleNurAdnan/CS_Checkpoints_Dataset") - Notebooks
- Google Colab
- Kaggle
| accuracy,precision,recall,f1_binary,f1_macro,f1_weighted,fpr,auc_roc,threshold,round,comm_mb,round_time_s | |
| 0.6699,0.6940055762081785,0.6008851337759002,0.6440970350404313,0.6681557436274417,0.6682952841372464,0.26188108967985685,0.763234135196308,0.6085902452468872,1,2.158,140.78 | |
| 0.6629,0.6569858712715856,0.6735063367531684,0.665143538293434,0.6628848669016751,0.6628717666076029,0.3475840127261881,0.7174012533781637,0.7601174116134644,2,2.158,139.75 | |
| 0.5827,0.5547926393847844,0.8127137396902032,0.6594303435893251,0.5603851085443721,0.5598106461811114,0.6446609663949096,0.5462027542606533,0.8770501613616943,3,2.158,142.11 | |
| 0.561,0.5741639009446005,0.45242405954536313,0.5060756075607561,0.5555036065161592,0.5557902889101005,0.33167627758997814,0.561915682843571,0.953309953212738,4,2.158,141.66 | |
| 0.6738,0.6396470011439778,0.7873667270166969,0.7058611361587015,0.6698778183599343,0.6696691151167015,0.4384569496917876,0.6984256750397084,0.9898511171340942,5,2.158,138.24 | |
| 0.701,0.690737531292124,0.7215851941259304,0.7058244785517512,0.700919559324676,0.7008911107931589,0.3193477828594154,0.7428818505454524,0.9979335069656372,6,2.158,135.44 | |
| 0.7249,0.694327731092437,0.7978273989136995,0.7424880651502387,0.7236106741979593,0.7235011853304361,0.3471863193477829,0.7839887933830094,0.9994715452194214,7,2.158,134.91 | |
| 0.7195,0.7089120370370371,0.7392878696439348,0.723781388478582,0.7194325936806318,0.7194073706708037,0.30005965400676077,0.7869276922475672,0.9998279809951782,8,2.158,133.3 | |
| 0.7494,0.7053140096618358,0.8517400925367129,0.7716420630581374,0.7469998482806752,0.7468569234349659,0.3517597931994432,0.7971441359287327,0.9999358654022217,9,2.158,132.05 | |
| 0.7817,0.7700503680743898,0.79963789981895,0.7845652817526892,0.7816613780811688,0.781644535439874,0.23603102008351562,0.8640437264309572,0.9999737739562988,10,2.158,132.91 | |
| 0.7604,0.7897816790456899,0.7058941862804264,0.7454854472062885,0.7595743880656418,0.7596561039226261,0.18572280771525154,0.8520362624998705,0.9999891519546509,11,2.158,132.66 | |
| 0.8571,0.925312199807877,0.7750955542144438,0.843568691844554,0.8560227260419273,0.856094959440272,0.0618413203420163,0.9162178415681904,0.9999955892562866,12,2.158,132.32 | |
| 0.8978,0.9434089377947451,0.8451015892174613,0.891553480475382,0.8974597962134853,0.8974940528447664,0.05010936567906144,0.9513063219446702,0.9999982118606567,13,2.158,133.13 | |
| 0.9301,0.9440748440748441,0.9134982900824784,0.9285349146304059,0.9300664591744847,0.9300753421328404,0.05348975939550606,0.9662367442040751,0.9999991655349731,14,2.158,133.26 | |
| 0.948,0.9485990727675871,0.9466908066787366,0.9476439790575916,0.9479975954088117,0.9479996463836488,0.05070590574666932,0.9852636242683205,0.9999994039535522,15,2.158,134.53 | |
| 0.9601,0.9466588511137163,0.9746529873264936,0.96045197740113,0.9600968392706386,0.9600947794694817,0.05428514615231656,0.9882923061531791,0.9999994039535522,16,2.158,134.67 | |
| 0.9973,0.997184231697506,0.9973848320257493,0.9972845217741124,0.9972999122741497,0.9973000015390499,0.0027838536488367467,0.9997200305818288,0.9999990463256836,17,2.158,134.45 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999997399912534,0.999998927116394,18,2.158,133.93 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999997399912532,0.9999994039535522,19,2.158,135.44 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999992599751055,0.9999985694885254,20,2.158,134.22 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999992999764512,0.9999991655349731,21,2.158,135.71 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999906396851189,0.9999991655349731,22,2.158,133.66 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999974999158971,0.9999990463256836,23,2.158,133.03 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999985999529024,0.999996542930603,24,2.158,132.84 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999993599784697,0.9999970197677612,25,2.158,133.1 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.999999099969723,0.9999924898147583,26,2.158,133.62 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999975399172429,0.9999964237213135,27,2.158,133.54 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999991399710687,0.9999983310699463,28,2.158,134.07 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.999999099969723,0.999998927116394,29,2.158,134.5 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999854995122036,0.9999593496322632,30,2.158,133.45 | |