--- license: bsd-3-clause base_model: Salesforce/codet5p-770m-py tags: - generated_from_trainer datasets: - mbpp model-index: - name: codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_18 results: [] library_name: peft --- # codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_18 This model is a fine-tuned version of [Salesforce/codet5p-770m-py](https://huggingface.co/Salesforce/codet5p-770m-py) on the mbpp dataset. It achieves the following results on the evaluation set: - Loss: 0.7028 - Codebleu: 0.1061 - Ngram Match Score: 0.0250 - Weighted Ngram Match Score: 0.0538 - Syntax Match Score: 0.1310 - Dataflow Match Score: 0.1145 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 64 ### Training results | Training Loss | Epoch | Step | Validation Loss | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:| | 0.9722 | 1.0 | 15 | 0.9246 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 | | 0.9623 | 2.0 | 30 | 0.9232 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 | | 0.9702 | 3.0 | 45 | 0.9197 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 | | 0.944 | 4.0 | 60 | 0.9118 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 | | 0.953 | 5.0 | 75 | 0.8956 | 0.0413 | 0.0007 | 0.0230 | 0.0370 | 0.0602 | | 0.9402 | 6.0 | 90 | 0.8719 | 0.0750 | 0.0134 | 0.0426 | 0.0913 | 0.0823 | | 0.9279 | 7.0 | 105 | 0.8508 | 0.0990 | 0.0215 | 0.0480 | 0.1217 | 0.1084 | | 0.8723 | 8.0 | 120 | 0.8362 | 0.0989 | 0.0221 | 0.0519 | 0.1243 | 0.1044 | | 0.8598 | 9.0 | 135 | 0.8227 | 0.0998 | 0.0231 | 0.0521 | 0.1243 | 0.1064 | | 0.8459 | 10.0 | 150 | 0.8105 | 0.1034 | 0.0148 | 0.0396 | 0.1283 | 0.1165 | | 0.8238 | 11.0 | 165 | 0.7952 | 0.0991 | 0.0162 | 0.0411 | 0.1190 | 0.1145 | | 0.8176 | 12.0 | 180 | 0.7802 | 0.1020 | 0.0185 | 0.0459 | 0.1243 | 0.1145 | | 0.7965 | 13.0 | 195 | 0.7719 | 0.1017 | 0.0173 | 0.0441 | 0.1243 | 0.1145 | | 0.7755 | 14.0 | 210 | 0.7616 | 0.1017 | 0.0181 | 0.0441 | 0.1243 | 0.1145 | | 0.7876 | 15.0 | 225 | 0.7538 | 0.1018 | 0.0186 | 0.0440 | 0.1243 | 0.1145 | | 0.7887 | 16.0 | 240 | 0.7509 | 0.0992 | 0.0174 | 0.0438 | 0.1204 | 0.1124 | | 0.7723 | 17.0 | 255 | 0.7467 | 0.1015 | 0.0182 | 0.0438 | 0.1217 | 0.1165 | | 0.753 | 18.0 | 270 | 0.7440 | 0.1015 | 0.0182 | 0.0438 | 0.1217 | 0.1165 | | 0.762 | 19.0 | 285 | 0.7405 | 0.1045 | 0.0200 | 0.0458 | 0.1243 | 0.1205 | | 0.7537 | 20.0 | 300 | 0.7377 | 0.1029 | 0.0182 | 0.0447 | 0.1230 | 0.1185 | | 0.7381 | 21.0 | 315 | 0.7344 | 0.1056 | 0.0188 | 0.0449 | 0.1296 | 0.1185 | | 0.7389 | 22.0 | 330 | 0.7314 | 0.1048 | 0.0189 | 0.0449 | 0.1296 | 0.1165 | | 0.7352 | 23.0 | 345 | 0.7301 | 0.1030 | 0.0189 | 0.0449 | 0.1270 | 0.1145 | | 0.7421 | 24.0 | 360 | 0.7288 | 0.1029 | 0.0188 | 0.0449 | 0.1270 | 0.1145 | | 0.7569 | 25.0 | 375 | 0.7271 | 0.1053 | 0.0190 | 0.0441 | 0.1270 | 0.1205 | | 0.7339 | 26.0 | 390 | 0.7256 | 0.1071 | 0.0233 | 0.0529 | 0.1323 | 0.1165 | | 0.7169 | 27.0 | 405 | 0.7238 | 0.1072 | 0.0236 | 0.0530 | 0.1323 | 0.1165 | | 0.7278 | 28.0 | 420 | 0.7224 | 0.1062 | 0.0242 | 0.0535 | 0.1296 | 0.1165 | | 0.7185 | 29.0 | 435 | 0.7215 | 0.1063 | 0.0250 | 0.0536 | 0.1296 | 0.1165 | | 0.713 | 30.0 | 450 | 0.7204 | 0.1098 | 0.0254 | 0.0533 | 0.1283 | 0.1265 | | 0.7145 | 31.0 | 465 | 0.7195 | 0.1101 | 0.0266 | 0.0551 | 0.1283 | 0.1265 | | 0.7128 | 32.0 | 480 | 0.7180 | 0.1149 | 0.0282 | 0.0588 | 0.1389 | 0.1265 | | 0.7131 | 33.0 | 495 | 0.7172 | 0.1140 | 0.0276 | 0.0588 | 0.1389 | 0.1245 | | 0.7116 | 34.0 | 510 | 0.7154 | 0.1140 | 0.0276 | 0.0588 | 0.1389 | 0.1245 | | 0.6959 | 35.0 | 525 | 0.7140 | 0.1148 | 0.0276 | 0.0588 | 0.1389 | 0.1265 | | 0.7259 | 36.0 | 540 | 0.7131 | 0.1148 | 0.0276 | 0.0588 | 0.1389 | 0.1265 | | 0.7094 | 37.0 | 555 | 0.7127 | 0.1148 | 0.0276 | 0.0588 | 0.1389 | 0.1265 | | 0.7151 | 38.0 | 570 | 0.7119 | 0.1148 | 0.0276 | 0.0588 | 0.1389 | 0.1265 | | 0.7082 | 39.0 | 585 | 0.7108 | 0.1140 | 0.0280 | 0.0586 | 0.1389 | 0.1245 | | 0.7101 | 40.0 | 600 | 0.7102 | 0.1136 | 0.0271 | 0.0576 | 0.1402 | 0.1225 | | 0.696 | 41.0 | 615 | 0.7098 | 0.1136 | 0.0271 | 0.0576 | 0.1402 | 0.1225 | | 0.6955 | 42.0 | 630 | 0.7090 | 0.1136 | 0.0271 | 0.0576 | 0.1402 | 0.1225 | | 0.6967 | 43.0 | 645 | 0.7083 | 0.1136 | 0.0271 | 0.0576 | 0.1402 | 0.1225 | | 0.7006 | 44.0 | 660 | 0.7077 | 0.1136 | 0.0271 | 0.0576 | 0.1402 | 0.1225 | | 0.6921 | 45.0 | 675 | 0.7072 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.6866 | 46.0 | 690 | 0.7069 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.6971 | 47.0 | 705 | 0.7067 | 0.1068 | 0.0247 | 0.0540 | 0.1310 | 0.1165 | | 0.6943 | 48.0 | 720 | 0.7065 | 0.1060 | 0.0248 | 0.0538 | 0.1310 | 0.1145 | | 0.6988 | 49.0 | 735 | 0.7060 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.6865 | 50.0 | 750 | 0.7056 | 0.1068 | 0.0245 | 0.0539 | 0.1310 | 0.1165 | | 0.7076 | 51.0 | 765 | 0.7053 | 0.1060 | 0.0248 | 0.0538 | 0.1310 | 0.1145 | | 0.7049 | 52.0 | 780 | 0.7050 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.7008 | 53.0 | 795 | 0.7045 | 0.1119 | 0.0266 | 0.0576 | 0.1402 | 0.1185 | | 0.6728 | 54.0 | 810 | 0.7043 | 0.1119 | 0.0266 | 0.0576 | 0.1402 | 0.1185 | | 0.6918 | 55.0 | 825 | 0.7040 | 0.1119 | 0.0266 | 0.0576 | 0.1402 | 0.1185 | | 0.6952 | 56.0 | 840 | 0.7037 | 0.1119 | 0.0266 | 0.0576 | 0.1402 | 0.1185 | | 0.6986 | 57.0 | 855 | 0.7034 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.687 | 58.0 | 870 | 0.7032 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.7026 | 59.0 | 885 | 0.7030 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.6817 | 60.0 | 900 | 0.7030 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.6994 | 61.0 | 915 | 0.7029 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.6843 | 62.0 | 930 | 0.7029 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.6876 | 63.0 | 945 | 0.7028 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | | 0.6783 | 64.0 | 960 | 0.7028 | 0.1061 | 0.0250 | 0.0538 | 0.1310 | 0.1145 | ### Framework versions - PEFT 0.4.0 - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3