codet5p-770m-py-sanitized-codebleu-1-True-0.0001-0.1-lora
This model is a fine-tuned version of Salesforce/codet5p-770m-py on the mbpp dataset. It achieves the following results on the evaluation set:
- Loss: 0.9830
- Codebleu: 0.1241
- Ngram Match Score: 0.0424
- Weighted Ngram Match Score: 0.0802
- Syntax Match Score: 0.1310
- Dataflow Match Score: 0.1486
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: 0.0001
- train_batch_size: 6
- eval_batch_size: 6
- 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: 50
Training results
Training Loss | Epoch | Step | Validation Loss | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score |
---|---|---|---|---|---|---|---|---|
0.9803 | 1.0 | 20 | 0.9122 | 0.0451 | 0.0009 | 0.0261 | 0.0437 | 0.0622 |
0.899 | 2.0 | 40 | 0.8500 | 0.0969 | 0.0231 | 0.0517 | 0.1190 | 0.1044 |
0.8246 | 3.0 | 60 | 0.7960 | 0.0972 | 0.0183 | 0.0489 | 0.1217 | 0.1044 |
0.7556 | 4.0 | 80 | 0.7513 | 0.1063 | 0.0145 | 0.0405 | 0.1296 | 0.1225 |
0.7133 | 5.0 | 100 | 0.7276 | 0.1098 | 0.0226 | 0.0515 | 0.1336 | 0.1225 |
0.6932 | 6.0 | 120 | 0.7113 | 0.1033 | 0.0225 | 0.0474 | 0.1243 | 0.1165 |
0.6379 | 7.0 | 140 | 0.7016 | 0.1050 | 0.0257 | 0.0531 | 0.1204 | 0.1225 |
0.5922 | 8.0 | 160 | 0.7023 | 0.1071 | 0.0283 | 0.0524 | 0.1230 | 0.1245 |
0.5445 | 9.0 | 180 | 0.7068 | 0.1050 | 0.0208 | 0.0449 | 0.1257 | 0.1205 |
0.5165 | 10.0 | 200 | 0.7123 | 0.1003 | 0.0234 | 0.0474 | 0.1085 | 0.1245 |
0.4817 | 11.0 | 220 | 0.7109 | 0.1013 | 0.0198 | 0.0405 | 0.1177 | 0.1205 |
0.4608 | 12.0 | 240 | 0.7234 | 0.1141 | 0.0304 | 0.0538 | 0.1257 | 0.1386 |
0.4323 | 13.0 | 260 | 0.7305 | 0.1071 | 0.0286 | 0.0502 | 0.1257 | 0.1225 |
0.403 | 14.0 | 280 | 0.7538 | 0.0976 | 0.0222 | 0.0402 | 0.1019 | 0.1265 |
0.3987 | 15.0 | 300 | 0.7431 | 0.0995 | 0.0232 | 0.0454 | 0.1111 | 0.1205 |
0.3701 | 16.0 | 320 | 0.7689 | 0.1028 | 0.0334 | 0.0602 | 0.1190 | 0.1145 |
0.3361 | 17.0 | 340 | 0.7819 | 0.1032 | 0.0361 | 0.0594 | 0.1177 | 0.1165 |
0.321 | 18.0 | 360 | 0.7912 | 0.1184 | 0.0491 | 0.0785 | 0.1296 | 0.1345 |
0.3079 | 19.0 | 380 | 0.8008 | 0.1193 | 0.0442 | 0.0736 | 0.1323 | 0.1365 |
0.2863 | 20.0 | 400 | 0.8250 | 0.1031 | 0.0333 | 0.0638 | 0.1230 | 0.1104 |
0.2753 | 21.0 | 420 | 0.8410 | 0.1129 | 0.0479 | 0.0779 | 0.1204 | 0.1305 |
0.2669 | 22.0 | 440 | 0.8477 | 0.1101 | 0.0377 | 0.0682 | 0.1283 | 0.1205 |
0.2492 | 23.0 | 460 | 0.8404 | 0.1058 | 0.0471 | 0.0767 | 0.1190 | 0.1145 |
0.2293 | 24.0 | 480 | 0.8766 | 0.1048 | 0.0417 | 0.0695 | 0.1177 | 0.1165 |
0.229 | 25.0 | 500 | 0.8893 | 0.1043 | 0.0382 | 0.0621 | 0.1111 | 0.1245 |
0.2172 | 26.0 | 520 | 0.8777 | 0.1122 | 0.0387 | 0.0698 | 0.1310 | 0.1225 |
0.2135 | 27.0 | 540 | 0.8836 | 0.0997 | 0.0343 | 0.0553 | 0.1164 | 0.1104 |
0.2083 | 28.0 | 560 | 0.8862 | 0.1079 | 0.0386 | 0.0696 | 0.1243 | 0.1185 |
0.1958 | 29.0 | 580 | 0.8723 | 0.1186 | 0.0378 | 0.0655 | 0.1362 | 0.1345 |
0.1901 | 30.0 | 600 | 0.9106 | 0.1097 | 0.0322 | 0.0593 | 0.1310 | 0.1205 |
0.186 | 31.0 | 620 | 0.8958 | 0.1059 | 0.0398 | 0.0692 | 0.1190 | 0.1185 |
0.1709 | 32.0 | 640 | 0.9341 | 0.1106 | 0.0349 | 0.0644 | 0.1190 | 0.1325 |
0.176 | 33.0 | 660 | 0.9269 | 0.1078 | 0.0379 | 0.0691 | 0.1204 | 0.1225 |
0.1667 | 34.0 | 680 | 0.9310 | 0.1281 | 0.0404 | 0.0672 | 0.1548 | 0.1386 |
0.1614 | 35.0 | 700 | 0.9354 | 0.1137 | 0.0419 | 0.0729 | 0.1270 | 0.1285 |
0.1668 | 36.0 | 720 | 0.9297 | 0.1172 | 0.0477 | 0.0812 | 0.1283 | 0.1325 |
0.1594 | 37.0 | 740 | 0.9562 | 0.1175 | 0.0465 | 0.0830 | 0.1310 | 0.1305 |
0.1521 | 38.0 | 760 | 0.9478 | 0.1116 | 0.0398 | 0.0756 | 0.1296 | 0.1205 |
0.1486 | 39.0 | 780 | 0.9575 | 0.1340 | 0.0473 | 0.0794 | 0.1508 | 0.1526 |
0.1423 | 40.0 | 800 | 0.9661 | 0.1333 | 0.0413 | 0.0785 | 0.1508 | 0.1526 |
0.139 | 41.0 | 820 | 0.9598 | 0.1204 | 0.0374 | 0.0758 | 0.1442 | 0.1285 |
0.1333 | 42.0 | 840 | 0.9761 | 0.1206 | 0.0424 | 0.0750 | 0.1296 | 0.1426 |
0.1293 | 43.0 | 860 | 0.9717 | 0.1247 | 0.0415 | 0.0768 | 0.1376 | 0.1446 |
0.132 | 44.0 | 880 | 0.9827 | 0.1140 | 0.0392 | 0.0760 | 0.1296 | 0.1265 |
0.125 | 45.0 | 900 | 0.9940 | 0.1126 | 0.0372 | 0.0755 | 0.1310 | 0.1225 |
0.1318 | 46.0 | 920 | 0.9871 | 0.1245 | 0.0414 | 0.0772 | 0.1349 | 0.1466 |
0.1272 | 47.0 | 940 | 0.9773 | 0.1211 | 0.0437 | 0.0817 | 0.1310 | 0.1406 |
0.1264 | 48.0 | 960 | 0.9781 | 0.1315 | 0.0408 | 0.0765 | 0.1468 | 0.1526 |
0.1239 | 49.0 | 980 | 0.9827 | 0.1255 | 0.0404 | 0.0774 | 0.1336 | 0.1506 |
0.1277 | 50.0 | 1000 | 0.9830 | 0.1241 | 0.0424 | 0.0802 | 0.1310 | 0.1486 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
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