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---
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_0_9_21_23
results: []
---
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# codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_0_9_21_23
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: 0.6910
- Codebleu: 0.1101
- Ngram Match Score: 0.0179
- Weighted Ngram Match Score: 0.0340
- Syntax Match Score: 0.1177
- Dataflow Match Score: 0.1446
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|
| 0.9819 | 1.0 | 15 | 0.9236 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
| 0.9665 | 2.0 | 30 | 0.9191 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
| 0.9678 | 3.0 | 45 | 0.9089 | 0.0080 | 0.0000 | 0.0001 | 0.0079 | 0.0120 |
| 0.9211 | 4.0 | 60 | 0.8882 | 0.0332 | 0.0001 | 0.0151 | 0.0331 | 0.0462 |
| 0.919 | 5.0 | 75 | 0.8541 | 0.0834 | 0.0115 | 0.0349 | 0.1005 | 0.0964 |
| 0.8921 | 6.0 | 90 | 0.8245 | 0.0982 | 0.0213 | 0.0512 | 0.1270 | 0.1004 |
| 0.8575 | 7.0 | 105 | 0.7992 | 0.0952 | 0.0189 | 0.0470 | 0.1190 | 0.1024 |
| 0.8053 | 8.0 | 120 | 0.7769 | 0.0968 | 0.0176 | 0.0458 | 0.1217 | 0.1044 |
| 0.7762 | 9.0 | 135 | 0.7629 | 0.0989 | 0.0153 | 0.0426 | 0.1204 | 0.1124 |
| 0.7671 | 10.0 | 150 | 0.7534 | 0.0992 | 0.0091 | 0.0277 | 0.1204 | 0.1185 |
| 0.7504 | 11.0 | 165 | 0.7458 | 0.1129 | 0.0143 | 0.0442 | 0.1310 | 0.1365 |
| 0.7502 | 12.0 | 180 | 0.7393 | 0.1187 | 0.0225 | 0.0576 | 0.1402 | 0.1365 |
| 0.7452 | 13.0 | 195 | 0.7351 | 0.1172 | 0.0230 | 0.0576 | 0.1362 | 0.1365 |
| 0.7128 | 14.0 | 210 | 0.7298 | 0.1170 | 0.0222 | 0.0563 | 0.1323 | 0.1406 |
| 0.7337 | 15.0 | 225 | 0.7247 | 0.1210 | 0.0245 | 0.0599 | 0.1389 | 0.1426 |
| 0.7374 | 16.0 | 240 | 0.7200 | 0.1216 | 0.0251 | 0.0595 | 0.1442 | 0.1386 |
| 0.722 | 17.0 | 255 | 0.7158 | 0.1140 | 0.0218 | 0.0513 | 0.1362 | 0.1305 |
| 0.7002 | 18.0 | 270 | 0.7133 | 0.1156 | 0.0227 | 0.0530 | 0.1415 | 0.1285 |
| 0.6985 | 19.0 | 285 | 0.7116 | 0.1198 | 0.0279 | 0.0582 | 0.1455 | 0.1325 |
| 0.6981 | 20.0 | 300 | 0.7091 | 0.1204 | 0.0279 | 0.0582 | 0.1468 | 0.1325 |
| 0.6788 | 21.0 | 315 | 0.7059 | 0.1165 | 0.0263 | 0.0562 | 0.1402 | 0.1305 |
| 0.6747 | 22.0 | 330 | 0.7020 | 0.1162 | 0.0275 | 0.0570 | 0.1389 | 0.1305 |
| 0.6621 | 23.0 | 345 | 0.7013 | 0.1178 | 0.0227 | 0.0481 | 0.1362 | 0.1406 |
| 0.6599 | 24.0 | 360 | 0.7005 | 0.1110 | 0.0228 | 0.0471 | 0.1336 | 0.1265 |
| 0.6726 | 25.0 | 375 | 0.6983 | 0.1158 | 0.0220 | 0.0470 | 0.1296 | 0.1426 |
| 0.6463 | 26.0 | 390 | 0.6932 | 0.1179 | 0.0237 | 0.0481 | 0.1362 | 0.1406 |
| 0.6269 | 27.0 | 405 | 0.6925 | 0.1150 | 0.0225 | 0.0467 | 0.1257 | 0.1446 |
| 0.6454 | 28.0 | 420 | 0.6932 | 0.1094 | 0.0225 | 0.0465 | 0.1177 | 0.1386 |
| 0.6241 | 29.0 | 435 | 0.6922 | 0.1111 | 0.0233 | 0.0469 | 0.1217 | 0.1386 |
| 0.6253 | 30.0 | 450 | 0.6928 | 0.1198 | 0.0229 | 0.0464 | 0.1296 | 0.1526 |
| 0.6226 | 31.0 | 465 | 0.6920 | 0.1120 | 0.0241 | 0.0467 | 0.1257 | 0.1365 |
| 0.6156 | 32.0 | 480 | 0.6925 | 0.1153 | 0.0245 | 0.0476 | 0.1257 | 0.1446 |
| 0.613 | 33.0 | 495 | 0.6916 | 0.1140 | 0.0251 | 0.0473 | 0.1243 | 0.1426 |
| 0.612 | 34.0 | 510 | 0.6892 | 0.1112 | 0.0215 | 0.0415 | 0.1257 | 0.1365 |
| 0.5982 | 35.0 | 525 | 0.6894 | 0.1115 | 0.0203 | 0.0399 | 0.1230 | 0.1406 |
| 0.6148 | 36.0 | 540 | 0.6897 | 0.1091 | 0.0208 | 0.0397 | 0.1190 | 0.1386 |
| 0.5948 | 37.0 | 555 | 0.6899 | 0.1068 | 0.0172 | 0.0333 | 0.1177 | 0.1365 |
| 0.5946 | 38.0 | 570 | 0.6900 | 0.1076 | 0.0182 | 0.0353 | 0.1190 | 0.1365 |
| 0.5992 | 39.0 | 585 | 0.6907 | 0.1125 | 0.0176 | 0.0343 | 0.1177 | 0.1506 |
| 0.5966 | 40.0 | 600 | 0.6912 | 0.1116 | 0.0183 | 0.0354 | 0.1230 | 0.1426 |
| 0.586 | 41.0 | 615 | 0.6918 | 0.1149 | 0.0181 | 0.0339 | 0.1257 | 0.1486 |
| 0.5936 | 42.0 | 630 | 0.6906 | 0.1116 | 0.0179 | 0.0333 | 0.1177 | 0.1486 |
| 0.5739 | 43.0 | 645 | 0.6911 | 0.1117 | 0.0177 | 0.0340 | 0.1217 | 0.1446 |
| 0.5809 | 44.0 | 660 | 0.6911 | 0.1119 | 0.0176 | 0.0335 | 0.1164 | 0.1506 |
| 0.5752 | 45.0 | 675 | 0.6917 | 0.1083 | 0.0169 | 0.0324 | 0.1098 | 0.1486 |
| 0.5794 | 46.0 | 690 | 0.6918 | 0.1216 | 0.0215 | 0.0415 | 0.1257 | 0.1627 |
| 0.5816 | 47.0 | 705 | 0.6917 | 0.1200 | 0.0217 | 0.0415 | 0.1257 | 0.1586 |
| 0.5738 | 48.0 | 720 | 0.6919 | 0.1160 | 0.0187 | 0.0358 | 0.1257 | 0.1506 |
| 0.5819 | 49.0 | 735 | 0.6911 | 0.1123 | 0.0180 | 0.0340 | 0.1190 | 0.1486 |
| 0.5672 | 50.0 | 750 | 0.6918 | 0.1110 | 0.0184 | 0.0345 | 0.1217 | 0.1426 |
| 0.5852 | 51.0 | 765 | 0.6915 | 0.1098 | 0.0179 | 0.0341 | 0.1190 | 0.1426 |
| 0.5721 | 52.0 | 780 | 0.6912 | 0.1144 | 0.0177 | 0.0342 | 0.1243 | 0.1486 |
| 0.5823 | 53.0 | 795 | 0.6906 | 0.1077 | 0.0183 | 0.0338 | 0.1138 | 0.1426 |
| 0.5503 | 54.0 | 810 | 0.6904 | 0.1112 | 0.0190 | 0.0355 | 0.1217 | 0.1426 |
| 0.5701 | 55.0 | 825 | 0.6905 | 0.1098 | 0.0179 | 0.0341 | 0.1190 | 0.1426 |
| 0.577 | 56.0 | 840 | 0.6908 | 0.1101 | 0.0179 | 0.0340 | 0.1177 | 0.1446 |
| 0.5759 | 57.0 | 855 | 0.6912 | 0.1099 | 0.0180 | 0.0343 | 0.1190 | 0.1426 |
| 0.5615 | 58.0 | 870 | 0.6911 | 0.1099 | 0.0180 | 0.0343 | 0.1190 | 0.1426 |
| 0.576 | 59.0 | 885 | 0.6910 | 0.1098 | 0.0179 | 0.0341 | 0.1190 | 0.1426 |
| 0.5577 | 60.0 | 900 | 0.6909 | 0.1099 | 0.0180 | 0.0343 | 0.1190 | 0.1426 |
| 0.5743 | 61.0 | 915 | 0.6910 | 0.1111 | 0.0188 | 0.0352 | 0.1217 | 0.1426 |
| 0.5576 | 62.0 | 930 | 0.6911 | 0.1098 | 0.0179 | 0.0341 | 0.1190 | 0.1426 |
| 0.5676 | 63.0 | 945 | 0.6910 | 0.1101 | 0.0179 | 0.0340 | 0.1177 | 0.1446 |
| 0.5567 | 64.0 | 960 | 0.6910 | 0.1101 | 0.0179 | 0.0340 | 0.1177 | 0.1446 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3
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