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 | |
| 0.7502,0.786028221142725,0.6835646751156709,0.7312244458790618,0.7489486697917633,0.7490514702904569,0.18393318751242793,0.8355480478363292,0.820759117603302,1,24.461,193.02 | |
| 0.7392,0.7391216352964987,0.7346610339971836,0.7368845843422115,0.7391798020838733,0.7391931143467751,0.2563133823821833,0.8130661115439923,0.9926531314849854,2,24.461,38.06 | |
| 0.7155,0.8227079538554949,0.5451619392476363,0.6557773744706594,0.7066701103286404,0.7069652881966165,0.11612646649433286,0.7631054508673671,0.999717652797699,3,24.461,30.56 | |
| 0.6928,0.7243090007087172,0.6167773083886542,0.666232073011734,0.6908411198700759,0.6909838523418542,0.2320540862994631,0.7358766148893249,0.9999547004699707,4,24.461,29.13 | |
| 0.6316,0.6227350753385467,0.6568094950714142,0.6393185823379675,0.6314312102370401,0.6313854634788547,0.3933187512427918,0.6644737328963746,0.9999758005142212,5,24.461,29.36 | |
| 0.6499,0.6095706618962433,0.8225709112854557,0.7002311841767275,0.6397442424909596,0.6393934182291822,0.5207794790216743,0.6357867078648526,0.9999810457229614,6,24.461,28.49 | |
| 0.6591,0.6077538631346578,0.8861396097364715,0.7210082658155332,0.6414448860243326,0.6409834184215437,0.5653211374030622,0.6352644902974536,0.9999794960021973,7,24.461,29.44 | |
| 0.6864,0.6261862192270664,0.9159122912894789,0.7438327070740075,0.6698024066434745,0.6693730309009774,0.5404653012527342,0.6851373680210603,0.9999806880950928,8,24.461,29.84 | |
| 0.7301,0.6744471744471745,0.8835244417622209,0.7649568927980492,0.7240306361371953,0.7237932638485624,0.4215549811095645,0.7813879858918454,0.9999788999557495,9,24.461,29.54 | |
| 0.764,0.8289241622574955,0.6618386642526655,0.7360178970917226,0.7613181709690078,0.761464912557496,0.13501690196858224,0.8443496839233671,0.9999819993972778,10,24.461,30.77 | |
| 0.8221,0.8765927324209533,0.7473345403339369,0.8068194157889022,0.8209799052811622,0.8210620361202174,0.10399681845297276,0.8931500055661873,0.9999821186065674,11,24.461,31.19 | |
| 0.8861,0.9737883283877349,0.792194729430698,0.873655019412091,0.8849840868567055,0.8850497954478844,0.021077749055478226,0.9383544862449172,0.9999698400497437,12,24.461,32.08 | |
| 0.9575,0.9875589875589875,0.9261717964192315,0.9558808263261704,0.9574426799712265,0.9574517387223678,0.011533107973752238,0.9936290056797511,0.9996058344841003,13,24.461,31.3 | |
| 0.9953,0.9977759805903761,0.9927579963789982,0.995260663507109,0.9952996761946931,0.9952999024682809,0.0021873135812288725,0.9997558117855083,0.9683486819267273,14,24.461,30.9 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999895396481138,0.0835861787199974,15,24.461,30.67 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999988199603034,0.2590314745903015,16,24.461,30.66 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999989799656861,0.7315090894699097,17,24.461,30.35 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999992999764513,0.971737802028656,18,24.461,31.03 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999986199535752,0.888298511505127,19,24.461,31.03 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.999998739957612,0.9276321530342102,20,24.461,31.04 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.999999099969723,0.942353367805481,21,24.461,30.78 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999996199872163,0.9703295826911926,22,24.461,30.57 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999996999899077,0.9668887853622437,23,24.461,30.37 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999996199872163,0.9688968062400818,24,24.461,30.48 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999996199872164,0.8118080496788025,25,24.461,30.74 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999992999764511,0.04107576608657837,26,24.461,30.29 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999996199872164,0.608148455619812,27,24.461,29.61 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999996199872164,0.9546127319335938,28,24.461,30.89 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999996199872163,0.8432609438896179,29,24.461,30.7 | |
| 0.9999,0.9997988736926791,1.0,0.9998994267323745,0.9998999967508944,0.9999000000570019,0.00019884668920262477,0.9999996199872163,0.752185583114624,30,24.461,30.39 | |