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codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_17

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metadata
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_17
    results: []
library_name: peft

codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_17

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: 0.7150
  • Codebleu: 0.0985
  • Ngram Match Score: 0.0231
  • Weighted Ngram Match Score: 0.0493
  • Syntax Match Score: 0.1217
  • Dataflow Match Score: 0.1064

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
0.9723 1.0 15 0.9246 0.0072 0.0000 0.0000 0.0079 0.0100
0.9649 2.0 30 0.9232 0.0072 0.0000 0.0000 0.0079 0.0100
0.9719 3.0 45 0.9202 0.0072 0.0000 0.0000 0.0079 0.0100
0.9435 4.0 60 0.9142 0.0080 0.0000 0.0000 0.0079 0.0120
0.9529 5.0 75 0.9023 0.0096 0.0000 0.0002 0.0079 0.0161
0.9443 6.0 90 0.8827 0.0380 0.0003 0.0176 0.0423 0.0482
0.9339 7.0 105 0.8558 0.0731 0.0080 0.0338 0.0899 0.0823
0.8783 8.0 120 0.8325 0.0960 0.0224 0.0519 0.1230 0.0984
0.8592 9.0 135 0.8139 0.0969 0.0188 0.0423 0.1164 0.1104
0.8398 10.0 150 0.7967 0.0972 0.0160 0.0379 0.1111 0.1185
0.8187 11.0 165 0.7857 0.1118 0.0199 0.0470 0.1362 0.1265
0.8064 12.0 180 0.7785 0.0996 0.0176 0.0449 0.1270 0.1064
0.7903 13.0 195 0.7729 0.1082 0.0179 0.0450 0.1402 0.1145
0.7737 14.0 210 0.7677 0.1076 0.0169 0.0457 0.1349 0.1185
0.7851 15.0 225 0.7628 0.1028 0.0163 0.0435 0.1296 0.1124
0.7865 16.0 240 0.7590 0.1031 0.0170 0.0435 0.1323 0.1104
0.7691 17.0 255 0.7552 0.0979 0.0175 0.0435 0.1230 0.1064
0.749 18.0 270 0.7522 0.0979 0.0176 0.0435 0.1230 0.1064
0.7587 19.0 285 0.7489 0.0950 0.0169 0.0419 0.1164 0.1064
0.7513 20.0 300 0.7465 0.0983 0.0174 0.0453 0.1217 0.1084
0.7368 21.0 315 0.7439 0.1015 0.0179 0.0452 0.1257 0.1124
0.7351 22.0 330 0.7411 0.1048 0.0195 0.0469 0.1310 0.1145
0.7347 23.0 345 0.7395 0.1011 0.0200 0.0471 0.1257 0.1104
0.7342 24.0 360 0.7384 0.1011 0.0200 0.0471 0.1257 0.1104
0.7561 25.0 375 0.7361 0.1075 0.0219 0.0503 0.1323 0.1185
0.7272 26.0 390 0.7345 0.1041 0.0211 0.0486 0.1323 0.1104
0.7172 27.0 405 0.7330 0.1007 0.0220 0.0485 0.1257 0.1084
0.724 28.0 420 0.7320 0.1007 0.0220 0.0485 0.1257 0.1084
0.7098 29.0 435 0.7316 0.1024 0.0231 0.0486 0.1257 0.1124
0.7111 30.0 450 0.7312 0.1033 0.0235 0.0492 0.1257 0.1145
0.7076 31.0 465 0.7299 0.1017 0.0229 0.0492 0.1257 0.1104
0.7104 32.0 480 0.7282 0.1029 0.0236 0.0502 0.1283 0.1104
0.7059 33.0 495 0.7272 0.1046 0.0242 0.0503 0.1283 0.1145
0.7082 34.0 510 0.7254 0.1029 0.0236 0.0502 0.1283 0.1104
0.6904 35.0 525 0.7245 0.1090 0.0244 0.0517 0.1349 0.1185
0.7197 36.0 540 0.7237 0.1029 0.0236 0.0502 0.1283 0.1104
0.7051 37.0 555 0.7231 0.1029 0.0236 0.0502 0.1283 0.1104
0.7083 38.0 570 0.7227 0.1029 0.0236 0.0502 0.1283 0.1104
0.7018 39.0 585 0.7216 0.0994 0.0239 0.0501 0.1217 0.1084
0.7033 40.0 600 0.7209 0.1000 0.0238 0.0503 0.1230 0.1084
0.6924 41.0 615 0.7208 0.0994 0.0239 0.0501 0.1217 0.1084
0.689 42.0 630 0.7209 0.1003 0.0239 0.0505 0.1217 0.1104
0.6935 43.0 645 0.7202 0.1034 0.0236 0.0505 0.1296 0.1104
0.6927 44.0 660 0.7197 0.1000 0.0238 0.0503 0.1230 0.1084
0.6877 45.0 675 0.7193 0.1034 0.0236 0.0505 0.1296 0.1104
0.6835 46.0 690 0.7188 0.1034 0.0236 0.0505 0.1296 0.1104
0.6889 47.0 705 0.7182 0.1034 0.0236 0.0505 0.1296 0.1104
0.6913 48.0 720 0.7182 0.1034 0.0236 0.0505 0.1296 0.1104
0.6874 49.0 735 0.7178 0.1034 0.0234 0.0505 0.1296 0.1104
0.6801 50.0 750 0.7175 0.1034 0.0234 0.0505 0.1296 0.1104
0.704 51.0 765 0.7172 0.1034 0.0234 0.0505 0.1296 0.1104
0.6981 52.0 780 0.7169 0.1034 0.0234 0.0505 0.1296 0.1104
0.6927 53.0 795 0.7164 0.1030 0.0226 0.0494 0.1310 0.1084
0.6638 54.0 810 0.7161 0.1030 0.0226 0.0494 0.1310 0.1084
0.6859 55.0 825 0.7159 0.1030 0.0226 0.0494 0.1310 0.1084
0.688 56.0 840 0.7157 0.1030 0.0226 0.0494 0.1310 0.1084
0.6903 57.0 855 0.7155 0.0995 0.0229 0.0493 0.1243 0.1064
0.6784 58.0 870 0.7153 0.0995 0.0229 0.0493 0.1243 0.1064
0.7028 59.0 885 0.7152 0.0995 0.0229 0.0493 0.1243 0.1064
0.6744 60.0 900 0.7152 0.1019 0.0228 0.0494 0.1283 0.1084
0.693 61.0 915 0.7151 0.0985 0.0231 0.0493 0.1217 0.1064
0.6801 62.0 930 0.7151 0.0985 0.0231 0.0493 0.1217 0.1064
0.6842 63.0 945 0.7150 0.0985 0.0231 0.0493 0.1217 0.1064
0.674 64.0 960 0.7150 0.0985 0.0231 0.0493 0.1217 0.1064

Framework versions

  • PEFT 0.4.0
  • Transformers 4.31.0
  • Pytorch 2.0.1
  • Datasets 2.14.4
  • Tokenizers 0.13.3