File size: 11,671 Bytes
e2f9ae5 6b81836 e2f9ae5 6b81836 e2f9ae5 6b81836 e2f9ae5 6b81836 e2f9ae5 6b81836 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 |
---
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_9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_9
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: 0.7314
- Codebleu: 0.1102
- Ngram Match Score: 0.0199
- Weighted Ngram Match Score: 0.0416
- Syntax Match Score: 0.1296
- Dataflow Match Score: 0.1305
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|
| 0.9846 | 1.0 | 15 | 0.9244 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
| 0.9657 | 2.0 | 30 | 0.9227 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
| 0.9697 | 3.0 | 45 | 0.9188 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 |
| 0.9408 | 4.0 | 60 | 0.9100 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 |
| 0.9463 | 5.0 | 75 | 0.8926 | 0.0386 | 0.0004 | 0.0207 | 0.0370 | 0.0542 |
| 0.9393 | 6.0 | 90 | 0.8669 | 0.0729 | 0.0083 | 0.0317 | 0.0820 | 0.0904 |
| 0.9176 | 7.0 | 105 | 0.8475 | 0.1012 | 0.0187 | 0.0481 | 0.1177 | 0.1185 |
| 0.8691 | 8.0 | 120 | 0.8337 | 0.1005 | 0.0185 | 0.0500 | 0.1217 | 0.1124 |
| 0.8468 | 9.0 | 135 | 0.8223 | 0.0984 | 0.0111 | 0.0331 | 0.1204 | 0.1145 |
| 0.8444 | 10.0 | 150 | 0.8119 | 0.1023 | 0.0100 | 0.0307 | 0.1230 | 0.1225 |
| 0.8293 | 11.0 | 165 | 0.8013 | 0.1010 | 0.0096 | 0.0318 | 0.1257 | 0.1165 |
| 0.8248 | 12.0 | 180 | 0.7905 | 0.1003 | 0.0101 | 0.0321 | 0.1217 | 0.1185 |
| 0.8103 | 13.0 | 195 | 0.7838 | 0.1017 | 0.0106 | 0.0323 | 0.1230 | 0.1205 |
| 0.7907 | 14.0 | 210 | 0.7778 | 0.1050 | 0.0109 | 0.0328 | 0.1270 | 0.1245 |
| 0.8004 | 15.0 | 225 | 0.7735 | 0.1066 | 0.0123 | 0.0369 | 0.1296 | 0.1245 |
| 0.7995 | 16.0 | 240 | 0.7694 | 0.1029 | 0.0114 | 0.0353 | 0.1230 | 0.1225 |
| 0.7906 | 17.0 | 255 | 0.7662 | 0.1010 | 0.0105 | 0.0329 | 0.1190 | 0.1225 |
| 0.766 | 18.0 | 270 | 0.7626 | 0.0976 | 0.0104 | 0.0313 | 0.1151 | 0.1185 |
| 0.7761 | 19.0 | 285 | 0.7592 | 0.0984 | 0.0105 | 0.0313 | 0.1190 | 0.1165 |
| 0.7647 | 20.0 | 300 | 0.7564 | 0.0941 | 0.0102 | 0.0310 | 0.1124 | 0.1124 |
| 0.7511 | 21.0 | 315 | 0.7532 | 0.1006 | 0.0126 | 0.0328 | 0.1177 | 0.1225 |
| 0.755 | 22.0 | 330 | 0.7510 | 0.0950 | 0.0116 | 0.0306 | 0.1124 | 0.1145 |
| 0.7491 | 23.0 | 345 | 0.7501 | 0.0963 | 0.0120 | 0.0303 | 0.1138 | 0.1165 |
| 0.7467 | 24.0 | 360 | 0.7485 | 0.1038 | 0.0133 | 0.0319 | 0.1296 | 0.1185 |
| 0.7709 | 25.0 | 375 | 0.7460 | 0.1038 | 0.0134 | 0.0319 | 0.1296 | 0.1185 |
| 0.7397 | 26.0 | 390 | 0.7445 | 0.1130 | 0.0175 | 0.0408 | 0.1415 | 0.1265 |
| 0.7266 | 27.0 | 405 | 0.7434 | 0.1131 | 0.0176 | 0.0408 | 0.1415 | 0.1265 |
| 0.7362 | 28.0 | 420 | 0.7420 | 0.1125 | 0.0176 | 0.0404 | 0.1402 | 0.1265 |
| 0.7254 | 29.0 | 435 | 0.7418 | 0.1125 | 0.0176 | 0.0404 | 0.1402 | 0.1265 |
| 0.722 | 30.0 | 450 | 0.7413 | 0.1151 | 0.0176 | 0.0403 | 0.1429 | 0.1305 |
| 0.7253 | 31.0 | 465 | 0.7406 | 0.1194 | 0.0184 | 0.0417 | 0.1468 | 0.1365 |
| 0.7271 | 32.0 | 480 | 0.7392 | 0.1194 | 0.0184 | 0.0417 | 0.1468 | 0.1365 |
| 0.7187 | 33.0 | 495 | 0.7382 | 0.1186 | 0.0184 | 0.0418 | 0.1468 | 0.1345 |
| 0.7253 | 34.0 | 510 | 0.7375 | 0.1193 | 0.0192 | 0.0433 | 0.1481 | 0.1345 |
| 0.6948 | 35.0 | 525 | 0.7367 | 0.1205 | 0.0212 | 0.0451 | 0.1481 | 0.1365 |
| 0.7327 | 36.0 | 540 | 0.7361 | 0.1212 | 0.0207 | 0.0449 | 0.1481 | 0.1386 |
| 0.7205 | 37.0 | 555 | 0.7357 | 0.1205 | 0.0212 | 0.0451 | 0.1481 | 0.1365 |
| 0.718 | 38.0 | 570 | 0.7356 | 0.1202 | 0.0203 | 0.0434 | 0.1481 | 0.1365 |
| 0.7128 | 39.0 | 585 | 0.7353 | 0.1202 | 0.0203 | 0.0434 | 0.1481 | 0.1365 |
| 0.7159 | 40.0 | 600 | 0.7350 | 0.1171 | 0.0204 | 0.0433 | 0.1442 | 0.1325 |
| 0.7016 | 41.0 | 615 | 0.7346 | 0.1171 | 0.0204 | 0.0433 | 0.1442 | 0.1325 |
| 0.7003 | 42.0 | 630 | 0.7343 | 0.1171 | 0.0204 | 0.0433 | 0.1442 | 0.1325 |
| 0.7018 | 43.0 | 645 | 0.7341 | 0.1171 | 0.0204 | 0.0433 | 0.1442 | 0.1325 |
| 0.7105 | 44.0 | 660 | 0.7346 | 0.1171 | 0.0204 | 0.0433 | 0.1442 | 0.1325 |
| 0.7084 | 45.0 | 675 | 0.7345 | 0.1133 | 0.0203 | 0.0432 | 0.1349 | 0.1325 |
| 0.6965 | 46.0 | 690 | 0.7342 | 0.1137 | 0.0203 | 0.0418 | 0.1323 | 0.1365 |
| 0.7062 | 47.0 | 705 | 0.7339 | 0.1137 | 0.0203 | 0.0418 | 0.1323 | 0.1365 |
| 0.6993 | 48.0 | 720 | 0.7339 | 0.1137 | 0.0203 | 0.0418 | 0.1323 | 0.1365 |
| 0.7039 | 49.0 | 735 | 0.7337 | 0.1111 | 0.0203 | 0.0420 | 0.1296 | 0.1325 |
| 0.6941 | 50.0 | 750 | 0.7335 | 0.1155 | 0.0199 | 0.0417 | 0.1389 | 0.1345 |
| 0.7144 | 51.0 | 765 | 0.7329 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.7027 | 52.0 | 780 | 0.7325 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.7042 | 53.0 | 795 | 0.7318 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.6756 | 54.0 | 810 | 0.7314 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.6979 | 55.0 | 825 | 0.7311 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.7013 | 56.0 | 840 | 0.7314 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.7 | 57.0 | 855 | 0.7313 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.6918 | 58.0 | 870 | 0.7313 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.7043 | 59.0 | 885 | 0.7312 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.6889 | 60.0 | 900 | 0.7313 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.7044 | 61.0 | 915 | 0.7314 | 0.1110 | 0.0198 | 0.0417 | 0.1296 | 0.1325 |
| 0.6901 | 62.0 | 930 | 0.7314 | 0.1102 | 0.0199 | 0.0416 | 0.1296 | 0.1305 |
| 0.6919 | 63.0 | 945 | 0.7313 | 0.1102 | 0.0199 | 0.0416 | 0.1296 | 0.1305 |
| 0.686 | 64.0 | 960 | 0.7314 | 0.1102 | 0.0199 | 0.0416 | 0.1296 | 0.1305 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
|