Instructions to use humanlong/improving-self-evolution-mbpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use humanlong/improving-self-evolution-mbpp with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("humanlong/improving-self-evolution-mbpp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| stage,method,round,samples,correct,pass_at_1,pass_at_1_ci_low,pass_at_1_ci_high,coverage_at_correct_4,coverage_ci_low,coverage_ci_high,coverage_eligible_tasks | |
| base/evaluation,base,0,8000,3034,0.37925,0.346871875,0.41400312499999997,3.310434709480511,3.221199169291293,3.400123345220875,262 | |
| plain/round_1/evaluation,plain,1,8000,3074,0.38425,0.351490625,0.418875,3.173048067300941,3.0671956385801002,3.280951416244543,261 | |
| plain/round_2/evaluation,plain,2,8000,3132,0.3915,0.359621875,0.42625625,3.0800160393097196,2.973781447839116,3.191841953132971,269 | |
| plain/round_3/evaluation,plain,3,8000,3172,0.3965,0.364990625,0.43238125,2.8954791209655024,2.776544808807758,3.018028129989538,257 | |
| plain/round_4/evaluation,plain,4,8000,3263,0.407875,0.374375,0.444375,2.803304432193321,2.685506626926781,2.918009932185873,270 | |
| plain/round_5/evaluation,plain,5,8000,3304,0.413,0.379375,0.44975624999999997,2.746691347624183,2.6286378087769147,2.8617909104231325,268 | |
| spd_hard/round_1/evaluation,spd_hard,1,8000,3034,0.37925,0.34699375,0.41325,3.14389616004402,3.0352207273691194,3.2531275232457144,257 | |
| spd_hard/round_2/evaluation,spd_hard,2,8000,3136,0.392,0.359871875,0.42638125,3.0590361365642265,2.9448345552533546,3.170826854123907,267 | |
| spd_hard/round_3/evaluation,spd_hard,3,8000,3152,0.394,0.362125,0.42963124999999996,2.888999737241925,2.7680368823190697,3.0101980898401095,256 | |
| spd_hard/round_4/evaluation,spd_hard,4,8000,3287,0.410875,0.376996875,0.448875,2.774081202840601,2.655616936002298,2.8878533549295806,266 | |
| spd_hard/round_5/evaluation,spd_hard,5,8000,3308,0.4135,0.379371875,0.451378125,2.689757274108419,2.5743440218177613,2.811019601389963,262 | |
| spectral_soft/round_1/evaluation,spectral_soft,1,8000,3027,0.378375,0.34675,0.41288125,3.221975831745947,3.1221283699493885,3.3256851813758965,261 | |
| spectral_soft/round_2/evaluation,spectral_soft,2,8000,3069,0.383625,0.351625,0.417878125,3.159304273589988,3.054195526187688,3.2671764167634643,259 | |
| spectral_soft/round_3/evaluation,spectral_soft,3,8000,3124,0.3905,0.35960624999999996,0.425375,3.0633024736078176,2.9517744700832935,3.174149546385129,262 | |
| spectral_soft/round_4/evaluation,spectral_soft,4,8000,3182,0.39775,0.36474375000000003,0.43275625,2.991413656052439,2.8799553352713403,3.1020587473730923,263 | |
| spectral_soft/round_5/evaluation,spectral_soft,5,8000,3214,0.40175,0.368490625,0.43750625,2.9971258357189914,2.888132498642703,3.1055915119398576,263 | |