--- 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_10 results: [] --- # codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_10 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: 0.7219 - Codebleu: 0.1061 - Ngram Match Score: 0.0202 - Weighted Ngram Match Score: 0.0460 - Syntax Match Score: 0.1283 - Dataflow Match Score: 0.1205 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:| | 0.984 | 1.0 | 15 | 0.9245 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 | | 0.9647 | 2.0 | 30 | 0.9229 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 | | 0.9735 | 3.0 | 45 | 0.9191 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 | | 0.9397 | 4.0 | 60 | 0.9110 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 | | 0.9453 | 5.0 | 75 | 0.8945 | 0.0337 | 0.0002 | 0.0146 | 0.0344 | 0.0462 | | 0.9376 | 6.0 | 90 | 0.8688 | 0.0807 | 0.0109 | 0.0349 | 0.0939 | 0.0964 | | 0.9196 | 7.0 | 105 | 0.8494 | 0.1005 | 0.0200 | 0.0510 | 0.1270 | 0.1064 | | 0.8634 | 8.0 | 120 | 0.8375 | 0.1001 | 0.0150 | 0.0392 | 0.1204 | 0.1165 | | 0.8494 | 9.0 | 135 | 0.8262 | 0.0977 | 0.0135 | 0.0369 | 0.1151 | 0.1165 | | 0.843 | 10.0 | 150 | 0.8134 | 0.0997 | 0.0135 | 0.0356 | 0.1164 | 0.1205 | | 0.8304 | 11.0 | 165 | 0.8006 | 0.1071 | 0.0129 | 0.0361 | 0.1310 | 0.1245 | | 0.8291 | 12.0 | 180 | 0.7911 | 0.1126 | 0.0158 | 0.0454 | 0.1376 | 0.1285 | | 0.811 | 13.0 | 195 | 0.7835 | 0.1124 | 0.0147 | 0.0447 | 0.1376 | 0.1285 | | 0.7917 | 14.0 | 210 | 0.7770 | 0.1122 | 0.0129 | 0.0444 | 0.1376 | 0.1285 | | 0.7976 | 15.0 | 225 | 0.7716 | 0.1108 | 0.0154 | 0.0444 | 0.1296 | 0.1325 | | 0.7978 | 16.0 | 240 | 0.7678 | 0.1049 | 0.0159 | 0.0427 | 0.1190 | 0.1285 | | 0.7899 | 17.0 | 255 | 0.7642 | 0.1008 | 0.0140 | 0.0386 | 0.1164 | 0.1225 | | 0.7663 | 18.0 | 270 | 0.7616 | 0.1051 | 0.0133 | 0.0369 | 0.1217 | 0.1285 | | 0.7762 | 19.0 | 285 | 0.7572 | 0.1011 | 0.0110 | 0.0279 | 0.1124 | 0.1305 | | 0.7656 | 20.0 | 300 | 0.7539 | 0.0938 | 0.0104 | 0.0279 | 0.1045 | 0.1205 | | 0.7518 | 21.0 | 315 | 0.7509 | 0.0967 | 0.0100 | 0.0278 | 0.1098 | 0.1225 | | 0.7484 | 22.0 | 330 | 0.7481 | 0.1046 | 0.0142 | 0.0363 | 0.1204 | 0.1285 | | 0.7448 | 23.0 | 345 | 0.7464 | 0.0990 | 0.0141 | 0.0361 | 0.1124 | 0.1225 | | 0.7439 | 24.0 | 360 | 0.7448 | 0.0964 | 0.0100 | 0.0277 | 0.1071 | 0.1245 | | 0.77 | 25.0 | 375 | 0.7420 | 0.1022 | 0.0107 | 0.0295 | 0.1190 | 0.1265 | | 0.7379 | 26.0 | 390 | 0.7410 | 0.0999 | 0.0146 | 0.0368 | 0.1124 | 0.1245 | | 0.7246 | 27.0 | 405 | 0.7400 | 0.0953 | 0.0120 | 0.0303 | 0.1111 | 0.1165 | | 0.738 | 28.0 | 420 | 0.7386 | 0.0969 | 0.0112 | 0.0292 | 0.1138 | 0.1185 | | 0.7263 | 29.0 | 435 | 0.7378 | 0.0955 | 0.0125 | 0.0296 | 0.1138 | 0.1145 | | 0.7185 | 30.0 | 450 | 0.7369 | 0.1007 | 0.0173 | 0.0391 | 0.1151 | 0.1225 | | 0.7167 | 31.0 | 465 | 0.7362 | 0.1007 | 0.0177 | 0.0391 | 0.1151 | 0.1225 | | 0.7191 | 32.0 | 480 | 0.7346 | 0.1044 | 0.0181 | 0.0410 | 0.1257 | 0.1205 | | 0.7128 | 33.0 | 495 | 0.7335 | 0.1051 | 0.0179 | 0.0407 | 0.1257 | 0.1225 | | 0.7156 | 34.0 | 510 | 0.7324 | 0.0980 | 0.0132 | 0.0322 | 0.1151 | 0.1185 | | 0.698 | 35.0 | 525 | 0.7314 | 0.0980 | 0.0132 | 0.0322 | 0.1151 | 0.1185 | | 0.7216 | 36.0 | 540 | 0.7312 | 0.1022 | 0.0137 | 0.0339 | 0.1230 | 0.1205 | | 0.7113 | 37.0 | 555 | 0.7308 | 0.1014 | 0.0145 | 0.0337 | 0.1230 | 0.1185 | | 0.7079 | 38.0 | 570 | 0.7305 | 0.1017 | 0.0200 | 0.0422 | 0.1243 | 0.1145 | | 0.7065 | 39.0 | 585 | 0.7294 | 0.1024 | 0.0179 | 0.0403 | 0.1230 | 0.1185 | | 0.7148 | 40.0 | 600 | 0.7286 | 0.1032 | 0.0174 | 0.0405 | 0.1230 | 0.1205 | | 0.695 | 41.0 | 615 | 0.7281 | 0.1032 | 0.0175 | 0.0405 | 0.1230 | 0.1205 | | 0.6948 | 42.0 | 630 | 0.7275 | 0.1025 | 0.0179 | 0.0408 | 0.1230 | 0.1185 | | 0.6973 | 43.0 | 645 | 0.7269 | 0.1045 | 0.0183 | 0.0421 | 0.1257 | 0.1205 | | 0.704 | 44.0 | 660 | 0.7267 | 0.1037 | 0.0187 | 0.0419 | 0.1257 | 0.1185 | | 0.7002 | 45.0 | 675 | 0.7262 | 0.1005 | 0.0183 | 0.0422 | 0.1217 | 0.1145 | | 0.6908 | 46.0 | 690 | 0.7258 | 0.1055 | 0.0192 | 0.0429 | 0.1296 | 0.1185 | | 0.7007 | 47.0 | 705 | 0.7256 | 0.1044 | 0.0192 | 0.0429 | 0.1270 | 0.1185 | | 0.693 | 48.0 | 720 | 0.7255 | 0.1044 | 0.0192 | 0.0429 | 0.1270 | 0.1185 | | 0.6955 | 49.0 | 735 | 0.7250 | 0.1052 | 0.0191 | 0.0426 | 0.1270 | 0.1205 | | 0.6849 | 50.0 | 750 | 0.7249 | 0.1058 | 0.0196 | 0.0461 | 0.1257 | 0.1225 | | 0.7053 | 51.0 | 765 | 0.7246 | 0.1058 | 0.0196 | 0.0461 | 0.1257 | 0.1225 | | 0.7013 | 52.0 | 780 | 0.7241 | 0.1053 | 0.0178 | 0.0425 | 0.1257 | 0.1225 | | 0.7008 | 53.0 | 795 | 0.7236 | 0.1053 | 0.0180 | 0.0425 | 0.1257 | 0.1225 | | 0.6707 | 54.0 | 810 | 0.7233 | 0.1053 | 0.0180 | 0.0425 | 0.1257 | 0.1225 | | 0.6929 | 55.0 | 825 | 0.7228 | 0.1064 | 0.0180 | 0.0425 | 0.1283 | 0.1225 | | 0.699 | 56.0 | 840 | 0.7228 | 0.1071 | 0.0176 | 0.0422 | 0.1283 | 0.1245 | | 0.7078 | 57.0 | 855 | 0.7224 | 0.1064 | 0.0180 | 0.0425 | 0.1283 | 0.1225 | | 0.6849 | 58.0 | 870 | 0.7222 | 0.1061 | 0.0202 | 0.0460 | 0.1283 | 0.1205 | | 0.7021 | 59.0 | 885 | 0.7220 | 0.1061 | 0.0202 | 0.0460 | 0.1283 | 0.1205 | | 0.6795 | 60.0 | 900 | 0.7220 | 0.1061 | 0.0202 | 0.0460 | 0.1283 | 0.1205 | | 0.703 | 61.0 | 915 | 0.7220 | 0.1061 | 0.0202 | 0.0460 | 0.1283 | 0.1205 | | 0.6849 | 62.0 | 930 | 0.7220 | 0.1061 | 0.0202 | 0.0460 | 0.1283 | 0.1205 | | 0.6889 | 63.0 | 945 | 0.7219 | 0.1061 | 0.0202 | 0.0460 | 0.1283 | 0.1205 | | 0.6813 | 64.0 | 960 | 0.7219 | 0.1061 | 0.0202 | 0.0460 | 0.1283 | 0.1205 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3