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 | |
| 0.8439,0.6749598715890851,0.42113169754631946,0.5186555658341042,0.7127502332363216,0.8293234904780935,0.05060602274147195,0.6817292663974094,0.5394453406333923,1,2.158,171.76 | |
| 0.8456,0.6974716652136007,0.400600901352028,0.5089058524173028,0.7086532109737202,0.8286214745227045,0.04335874047232288,0.6766077830978632,0.6490857601165771,2,2.158,170.94 | |
| 0.853,0.731750219876869,0.4166249374061092,0.5309508615188258,0.7218966331784809,0.8365786636372697,0.03811070848431838,0.7010308039843096,0.7225668430328369,3,2.158,171.14 | |
| 0.8659,0.8266932270916335,0.4156234351527291,0.5531489503498833,0.7371309784986666,0.8476305846048259,0.02174184680744721,0.8370002836317454,0.765577495098114,4,2.158,171.28 | |
| 0.8724,0.8905742145178764,0.4116174261392088,0.563013698630137,0.7441532193384877,0.8529456154759231,0.012620267399725103,0.8432748773291139,0.7975497841835022,5,2.158,172.34 | |
| 0.8734,0.9003285870755751,0.4116174261392088,0.5649484536082474,0.7454350225911337,0.8538352559222551,0.011370735974009746,0.8457642730495845,0.8343972563743591,6,2.158,173.3 | |
| 0.8749,0.9172259507829977,0.41061592388582874,0.5672777585610516,0.7470791738623248,0.8550679038922695,0.00924653255029364,0.8637211408766279,0.8631512522697449,7,2.158,174.1 | |
| 0.8758,0.9265536723163842,0.41061592388582874,0.5690492713393477,0.7482470331460145,0.8558732088870987,0.008121954267149819,0.8712795858788809,0.8876856565475464,8,2.158,170.44 | |
| 0.8773,0.9435483870967742,0.4101151727591387,0.5717277486910994,0.7500599642200756,0.8571662928667785,0.006122703986005248,0.8789044493893159,0.9097426533699036,9,2.158,168.89 | |
| 0.8684,0.7372822299651568,0.5297946920380571,0.6165501165501166,0.7685599448033056,0.8598570476521709,0.047107334749468946,0.8719161461172141,0.8947171568870544,10,2.158,168.91 | |
| 0.869,0.6769706336939721,0.657986980470706,0.6673438293550026,0.7928924351606292,0.8682969278074885,0.07834562039235286,0.8857386104146849,0.9025761485099792,11,2.158,167.68 | |
| 0.8887,0.7203389830508474,0.7235853780671007,0.7219585311016737,0.8261914928434542,0.8887938096655676,0.07009871298263151,0.8947316388802872,0.9217782616615295,12,2.158,166.76 | |
| 0.9018,0.7680929741151611,0.728092138207311,0.7475578406169666,0.8433009563109662,0.9008042715967823,0.05485442958890416,0.9026774261104263,0.9290059804916382,13,2.158,164.53 | |
| 0.9229,0.8475056689342404,0.7486229344016024,0.7950013294336613,0.8737615182176619,0.9210648876013326,0.033612395351743096,0.9498420128005327,0.936614990234375,14,2.158,163.36 | |
| 0.9282,0.8277806253203486,0.8087130696044066,0.8181357649442755,0.8867030681187862,0.9278845904053973,0.04198425590403598,0.96528639641957,0.937817394733429,15,2.158,163.38 | |
| 0.9376,0.8490086426029486,0.8362543815723585,0.8425832492431887,0.9018354011244629,0.9374222435443562,0.0371110833437461,0.974495918562337,0.9300932884216309,16,2.158,162.33 | |
| 0.9388,0.8090138331102187,0.9078617926890336,0.855592260500236,0.9083823502729578,0.9400880781904545,0.053479945020617266,0.9802218009007763,0.9367426037788391,17,2.158,165.4 | |
| 0.9436,0.808437365475678,0.9404106159238859,0.8694444444444445,0.9167375283446713,0.9451417545351475,0.055604148444333375,0.9836153080051164,0.9205788373947144,18,2.158,161.83 | |
| 0.9502,0.8347476552032157,0.9359038557836755,0.8824362606232294,0.9254226469317619,0.9512402705486663,0.0462326627514682,0.9860952868763349,0.8481175899505615,19,2.158,164.42 | |
| 0.9532,0.8514942528735632,0.927391086629945,0.887823585810163,0.9291278656874924,0.9539352161818164,0.04035986505060602,0.9871171245184658,0.8446512222290039,20,2.158,163.89 | |
| 0.9532,0.8587517597372126,0.9163745618427641,0.8866279069767442,0.928571009939985,0.9537620375797075,0.03761089591403224,0.9869294132376875,0.8579951524734497,21,2.158,162.81 | |
| 0.9536,0.8546968995835261,0.9248873309964948,0.8884078884078884,0.9295593286251032,0.9542748836195624,0.0392352867674622,0.9874266604204696,0.8702739477157593,22,2.158,164.54 | |
| 0.9543,0.8571428571428571,0.9253880821231848,0.8899590657356128,0.9305603999088867,0.9549455612133549,0.03848556791203299,0.9877975153408608,0.8766806125640869,23,2.158,163.79 | |
| 0.9557,0.8557692307692307,0.9359038557836755,0.894044486964841,0.9330200941683046,0.9564288438547048,0.039360239910033734,0.987943116724318,0.8477987051010132,24,2.158,163.26 | |
| 0.9564,0.8655737704918033,0.9253880821231848,0.8944820909970959,0.9335027041826921,0.9569384844619614,0.03586155191803074,0.9879530654221992,0.9019176363945007,25,2.158,164.22 | |
| 0.957,0.8585812356979405,0.9394091136705057,0.8971783835485414,0.9349970815201298,0.9577109915218658,0.038610521054604524,0.9883002687212126,0.7922727465629578,26,2.158,164.64 | |
| 0.9572,0.8600275355667738,0.9384076114171257,0.8975095785440613,0.9352310278969043,0.9578865303782217,0.03811070848431838,0.9886662431483035,0.7608827948570251,27,2.158,163.89 | |
| 0.9579,0.866852886405959,0.9323985978968453,0.898431845597105,0.9359393538928444,0.9584663633752656,0.035736598775459205,0.9888495119287702,0.8682874441146851,28,2.158,164.07 | |
| 0.9578,0.8702397743300423,0.9268903355032548,0.8976721629485936,0.9355455800885573,0.9582923544228196,0.03448706734974385,0.9887442059002536,0.8795708417892456,29,2.158,164.05 | |
| 0.958,0.856625961103573,0.9484226339509264,0.9001901140684411,0.936797184693795,0.9587833913113826,0.03961014619517681,0.9890123201796323,0.804903507232666,30,2.158,164.77 | |