--- 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_8 results: [] --- # codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_8 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: 0.7279 - Codebleu: 0.1015 - Ngram Match Score: 0.0151 - Weighted Ngram Match Score: 0.0370 - Syntax Match Score: 0.1243 - Dataflow Match Score: 0.1165 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:| | 0.9839 | 1.0 | 15 | 0.9244 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 | | 0.964 | 2.0 | 30 | 0.9227 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 | | 0.9714 | 3.0 | 45 | 0.9186 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 | | 0.942 | 4.0 | 60 | 0.9095 | 0.0096 | 0.0000 | 0.0001 | 0.0079 | 0.0161 | | 0.9486 | 5.0 | 75 | 0.8923 | 0.0440 | 0.0013 | 0.0232 | 0.0397 | 0.0643 | | 0.9432 | 6.0 | 90 | 0.8695 | 0.0861 | 0.0134 | 0.0388 | 0.1019 | 0.1004 | | 0.9245 | 7.0 | 105 | 0.8532 | 0.1013 | 0.0182 | 0.0479 | 0.1204 | 0.1165 | | 0.8732 | 8.0 | 120 | 0.8415 | 0.1006 | 0.0210 | 0.0506 | 0.1190 | 0.1145 | | 0.8547 | 9.0 | 135 | 0.8311 | 0.1057 | 0.0195 | 0.0508 | 0.1283 | 0.1185 | | 0.8553 | 10.0 | 150 | 0.8204 | 0.1054 | 0.0133 | 0.0380 | 0.1283 | 0.1225 | | 0.846 | 11.0 | 165 | 0.8089 | 0.1024 | 0.0112 | 0.0339 | 0.1243 | 0.1205 | | 0.8412 | 12.0 | 180 | 0.7983 | 0.0967 | 0.0098 | 0.0337 | 0.1204 | 0.1104 | | 0.8242 | 13.0 | 195 | 0.7914 | 0.0965 | 0.0109 | 0.0337 | 0.1177 | 0.1124 | | 0.8013 | 14.0 | 210 | 0.7851 | 0.0972 | 0.0108 | 0.0355 | 0.1151 | 0.1165 | | 0.8161 | 15.0 | 225 | 0.7800 | 0.0994 | 0.0120 | 0.0369 | 0.1177 | 0.1185 | | 0.8169 | 16.0 | 240 | 0.7751 | 0.1013 | 0.0125 | 0.0369 | 0.1204 | 0.1205 | | 0.8037 | 17.0 | 255 | 0.7708 | 0.0970 | 0.0116 | 0.0347 | 0.1164 | 0.1145 | | 0.7783 | 18.0 | 270 | 0.7663 | 0.0992 | 0.0129 | 0.0368 | 0.1190 | 0.1165 | | 0.7915 | 19.0 | 285 | 0.7622 | 0.0958 | 0.0141 | 0.0366 | 0.1085 | 0.1185 | | 0.7733 | 20.0 | 300 | 0.7578 | 0.0942 | 0.0150 | 0.0430 | 0.1085 | 0.1124 | | 0.763 | 21.0 | 315 | 0.7542 | 0.0942 | 0.0150 | 0.0430 | 0.1085 | 0.1124 | | 0.7627 | 22.0 | 330 | 0.7511 | 0.0941 | 0.0147 | 0.0429 | 0.1085 | 0.1124 | | 0.759 | 23.0 | 345 | 0.7490 | 0.0976 | 0.0153 | 0.0451 | 0.1124 | 0.1165 | | 0.7555 | 24.0 | 360 | 0.7471 | 0.0976 | 0.0152 | 0.0453 | 0.1124 | 0.1165 | | 0.7834 | 25.0 | 375 | 0.7447 | 0.1033 | 0.0168 | 0.0452 | 0.1204 | 0.1225 | | 0.7438 | 26.0 | 390 | 0.7429 | 0.1011 | 0.0148 | 0.0409 | 0.1204 | 0.1185 | | 0.7364 | 27.0 | 405 | 0.7412 | 0.0963 | 0.0133 | 0.0368 | 0.1138 | 0.1145 | | 0.7408 | 28.0 | 420 | 0.7401 | 0.1055 | 0.0160 | 0.0410 | 0.1270 | 0.1225 | | 0.7302 | 29.0 | 435 | 0.7393 | 0.0996 | 0.0146 | 0.0369 | 0.1177 | 0.1185 | | 0.7245 | 30.0 | 450 | 0.7388 | 0.0982 | 0.0153 | 0.0383 | 0.1177 | 0.1145 | | 0.7263 | 31.0 | 465 | 0.7376 | 0.0985 | 0.0157 | 0.0384 | 0.1204 | 0.1124 | | 0.7294 | 32.0 | 480 | 0.7360 | 0.0976 | 0.0150 | 0.0381 | 0.1204 | 0.1104 | | 0.7216 | 33.0 | 495 | 0.7349 | 0.0975 | 0.0158 | 0.0384 | 0.1177 | 0.1124 | | 0.7317 | 34.0 | 510 | 0.7340 | 0.0975 | 0.0158 | 0.0384 | 0.1177 | 0.1124 | | 0.7035 | 35.0 | 525 | 0.7337 | 0.0966 | 0.0152 | 0.0381 | 0.1177 | 0.1104 | | 0.7316 | 36.0 | 540 | 0.7335 | 0.0966 | 0.0152 | 0.0381 | 0.1177 | 0.1104 | | 0.7181 | 37.0 | 555 | 0.7330 | 0.0963 | 0.0143 | 0.0363 | 0.1177 | 0.1104 | | 0.7204 | 38.0 | 570 | 0.7330 | 0.0963 | 0.0143 | 0.0363 | 0.1177 | 0.1104 | | 0.7109 | 39.0 | 585 | 0.7324 | 0.0974 | 0.0142 | 0.0363 | 0.1204 | 0.1104 | | 0.7226 | 40.0 | 600 | 0.7321 | 0.0974 | 0.0142 | 0.0363 | 0.1204 | 0.1104 | | 0.703 | 41.0 | 615 | 0.7319 | 0.0976 | 0.0151 | 0.0381 | 0.1204 | 0.1104 | | 0.7036 | 42.0 | 630 | 0.7312 | 0.0976 | 0.0151 | 0.0381 | 0.1204 | 0.1104 | | 0.7039 | 43.0 | 645 | 0.7312 | 0.0993 | 0.0153 | 0.0384 | 0.1204 | 0.1145 | | 0.709 | 44.0 | 660 | 0.7309 | 0.1002 | 0.0159 | 0.0387 | 0.1204 | 0.1165 | | 0.7077 | 45.0 | 675 | 0.7308 | 0.0999 | 0.0147 | 0.0369 | 0.1204 | 0.1165 | | 0.7035 | 46.0 | 690 | 0.7303 | 0.0999 | 0.0147 | 0.0369 | 0.1204 | 0.1165 | | 0.7046 | 47.0 | 705 | 0.7299 | 0.0999 | 0.0147 | 0.0369 | 0.1204 | 0.1165 | | 0.7034 | 48.0 | 720 | 0.7298 | 0.0999 | 0.0147 | 0.0369 | 0.1204 | 0.1165 | | 0.7025 | 49.0 | 735 | 0.7296 | 0.0991 | 0.0146 | 0.0366 | 0.1204 | 0.1145 | | 0.691 | 50.0 | 750 | 0.7297 | 0.1007 | 0.0146 | 0.0366 | 0.1204 | 0.1185 | | 0.7124 | 51.0 | 765 | 0.7297 | 0.1004 | 0.0146 | 0.0367 | 0.1217 | 0.1165 | | 0.709 | 52.0 | 780 | 0.7293 | 0.1004 | 0.0146 | 0.0367 | 0.1217 | 0.1165 | | 0.7056 | 53.0 | 795 | 0.7288 | 0.1004 | 0.0146 | 0.0367 | 0.1217 | 0.1165 | | 0.6742 | 54.0 | 810 | 0.7286 | 0.1004 | 0.0146 | 0.0367 | 0.1217 | 0.1165 | | 0.7027 | 55.0 | 825 | 0.7282 | 0.1004 | 0.0146 | 0.0367 | 0.1217 | 0.1165 | | 0.7 | 56.0 | 840 | 0.7283 | 0.1004 | 0.0148 | 0.0367 | 0.1217 | 0.1165 | | 0.7074 | 57.0 | 855 | 0.7282 | 0.1015 | 0.0151 | 0.0370 | 0.1243 | 0.1165 | | 0.6869 | 58.0 | 870 | 0.7280 | 0.1004 | 0.0148 | 0.0367 | 0.1217 | 0.1165 | | 0.7054 | 59.0 | 885 | 0.7280 | 0.1015 | 0.0151 | 0.0370 | 0.1243 | 0.1165 | | 0.6902 | 60.0 | 900 | 0.7280 | 0.1004 | 0.0148 | 0.0367 | 0.1217 | 0.1165 | | 0.7107 | 61.0 | 915 | 0.7280 | 0.1015 | 0.0151 | 0.0370 | 0.1243 | 0.1165 | | 0.6963 | 62.0 | 930 | 0.7280 | 0.1015 | 0.0151 | 0.0370 | 0.1243 | 0.1165 | | 0.6958 | 63.0 | 945 | 0.7279 | 0.1015 | 0.0151 | 0.0370 | 0.1243 | 0.1165 | | 0.6883 | 64.0 | 960 | 0.7279 | 0.1015 | 0.0151 | 0.0370 | 0.1243 | 0.1165 | ### Framework versions - Transformers 4.31.0 - Pytorch 2.0.1 - Datasets 2.14.4 - Tokenizers 0.13.3