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codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_16

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- library_name: peft
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Training procedure
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- ### Framework versions
 
 
 
 
 
 
 
 
 
 
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- - PEFT 0.4.0
 
 
 
 
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+ license: bsd-3-clause
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+ base_model: Salesforce/codet5p-770m-py
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - mbpp
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+ model-index:
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+ - name: codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_16
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+ results: []
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  ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_16
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+
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+ This model is a fine-tuned version of [Salesforce/codet5p-770m-py](https://huggingface.co/Salesforce/codet5p-770m-py) on the mbpp dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.7124
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+ - Codebleu: 0.1101
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+ - Ngram Match Score: 0.0290
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+ - Weighted Ngram Match Score: 0.0603
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+ - Syntax Match Score: 0.1283
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+ - Dataflow Match Score: 0.1245
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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  ## Training procedure
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 100
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+ - num_epochs: 64
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Codebleu | Ngram Match Score | Weighted Ngram Match Score | Syntax Match Score | Dataflow Match Score |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:--------------------------:|:------------------:|:--------------------:|
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+ | 0.9725 | 1.0 | 15 | 0.9245 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.9641 | 2.0 | 30 | 0.9228 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.9703 | 3.0 | 45 | 0.9190 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.9422 | 4.0 | 60 | 0.9108 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 |
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+ | 0.9459 | 5.0 | 75 | 0.8947 | 0.0298 | 0.0001 | 0.0149 | 0.0265 | 0.0442 |
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+ | 0.936 | 6.0 | 90 | 0.8704 | 0.0692 | 0.0034 | 0.0282 | 0.0807 | 0.0843 |
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+ | 0.9197 | 7.0 | 105 | 0.8488 | 0.0998 | 0.0209 | 0.0519 | 0.1270 | 0.1044 |
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+ | 0.8658 | 8.0 | 120 | 0.8350 | 0.1016 | 0.0224 | 0.0521 | 0.1270 | 0.1084 |
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+ | 0.8532 | 9.0 | 135 | 0.8216 | 0.0986 | 0.0223 | 0.0515 | 0.1217 | 0.1064 |
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+ | 0.842 | 10.0 | 150 | 0.8080 | 0.0997 | 0.0137 | 0.0362 | 0.1204 | 0.1165 |
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+ | 0.8198 | 11.0 | 165 | 0.7945 | 0.1019 | 0.0130 | 0.0350 | 0.1204 | 0.1225 |
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+ | 0.814 | 12.0 | 180 | 0.7816 | 0.1018 | 0.0135 | 0.0354 | 0.1177 | 0.1245 |
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+ | 0.7963 | 13.0 | 195 | 0.7727 | 0.0975 | 0.0111 | 0.0321 | 0.1204 | 0.1124 |
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+ | 0.7768 | 14.0 | 210 | 0.7646 | 0.0931 | 0.0121 | 0.0329 | 0.1151 | 0.1064 |
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+ | 0.7844 | 15.0 | 225 | 0.7586 | 0.0971 | 0.0123 | 0.0332 | 0.1190 | 0.1124 |
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+ | 0.7847 | 16.0 | 240 | 0.7551 | 0.1025 | 0.0125 | 0.0331 | 0.1243 | 0.1205 |
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+ | 0.7708 | 17.0 | 255 | 0.7529 | 0.1054 | 0.0159 | 0.0409 | 0.1310 | 0.1185 |
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+ | 0.7464 | 18.0 | 270 | 0.7505 | 0.1100 | 0.0165 | 0.0413 | 0.1402 | 0.1205 |
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+ | 0.7569 | 19.0 | 285 | 0.7477 | 0.1084 | 0.0177 | 0.0445 | 0.1389 | 0.1165 |
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+ | 0.7486 | 20.0 | 300 | 0.7451 | 0.1110 | 0.0248 | 0.0579 | 0.1362 | 0.1205 |
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+ | 0.7348 | 21.0 | 315 | 0.7425 | 0.1102 | 0.0253 | 0.0577 | 0.1362 | 0.1185 |
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+ | 0.7282 | 22.0 | 330 | 0.7400 | 0.1120 | 0.0263 | 0.0584 | 0.1362 | 0.1225 |
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+ | 0.7291 | 23.0 | 345 | 0.7387 | 0.1095 | 0.0259 | 0.0578 | 0.1323 | 0.1205 |
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+ | 0.7316 | 24.0 | 360 | 0.7369 | 0.1094 | 0.0258 | 0.0573 | 0.1323 | 0.1205 |
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+ | 0.744 | 25.0 | 375 | 0.7350 | 0.1078 | 0.0259 | 0.0596 | 0.1336 | 0.1145 |
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+ | 0.7198 | 26.0 | 390 | 0.7328 | 0.1136 | 0.0277 | 0.0632 | 0.1429 | 0.1185 |
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+ | 0.7068 | 27.0 | 405 | 0.7308 | 0.1139 | 0.0277 | 0.0632 | 0.1376 | 0.1245 |
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+ | 0.7157 | 28.0 | 420 | 0.7294 | 0.1074 | 0.0278 | 0.0594 | 0.1283 | 0.1185 |
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+ | 0.6982 | 29.0 | 435 | 0.7283 | 0.1140 | 0.0281 | 0.0632 | 0.1376 | 0.1245 |
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+ | 0.7011 | 30.0 | 450 | 0.7273 | 0.1140 | 0.0281 | 0.0632 | 0.1376 | 0.1245 |
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+ | 0.6985 | 31.0 | 465 | 0.7263 | 0.1157 | 0.0298 | 0.0633 | 0.1376 | 0.1285 |
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+ | 0.6963 | 32.0 | 480 | 0.7253 | 0.1076 | 0.0295 | 0.0596 | 0.1243 | 0.1225 |
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+ | 0.6962 | 33.0 | 495 | 0.7245 | 0.1093 | 0.0300 | 0.0596 | 0.1243 | 0.1265 |
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+ | 0.6947 | 34.0 | 510 | 0.7231 | 0.1159 | 0.0309 | 0.0639 | 0.1336 | 0.1325 |
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+ | 0.682 | 35.0 | 525 | 0.7220 | 0.1143 | 0.0311 | 0.0639 | 0.1336 | 0.1285 |
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+ | 0.7081 | 36.0 | 540 | 0.7213 | 0.1145 | 0.0306 | 0.0639 | 0.1362 | 0.1265 |
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+ | 0.6964 | 37.0 | 555 | 0.7208 | 0.1170 | 0.0313 | 0.0639 | 0.1362 | 0.1325 |
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+ | 0.6975 | 38.0 | 570 | 0.7206 | 0.1179 | 0.0315 | 0.0639 | 0.1362 | 0.1345 |
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+ | 0.6873 | 39.0 | 585 | 0.7194 | 0.1142 | 0.0308 | 0.0634 | 0.1376 | 0.1245 |
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+ | 0.6962 | 40.0 | 600 | 0.7187 | 0.1178 | 0.0311 | 0.0639 | 0.1362 | 0.1345 |
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+ | 0.6816 | 41.0 | 615 | 0.7184 | 0.1178 | 0.0311 | 0.0639 | 0.1362 | 0.1345 |
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+ | 0.6781 | 42.0 | 630 | 0.7177 | 0.1151 | 0.0313 | 0.0634 | 0.1376 | 0.1265 |
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+ | 0.6836 | 43.0 | 645 | 0.7171 | 0.1167 | 0.0313 | 0.0634 | 0.1376 | 0.1305 |
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+ | 0.6826 | 44.0 | 660 | 0.7165 | 0.1151 | 0.0313 | 0.0634 | 0.1376 | 0.1265 |
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+ | 0.6747 | 45.0 | 675 | 0.7163 | 0.1171 | 0.0316 | 0.0639 | 0.1362 | 0.1325 |
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+ | 0.6735 | 46.0 | 690 | 0.7160 | 0.1171 | 0.0316 | 0.0639 | 0.1362 | 0.1325 |
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+ | 0.6752 | 47.0 | 705 | 0.7154 | 0.1153 | 0.0306 | 0.0638 | 0.1362 | 0.1285 |
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+ | 0.6809 | 48.0 | 720 | 0.7152 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6829 | 49.0 | 735 | 0.7147 | 0.1081 | 0.0287 | 0.0598 | 0.1296 | 0.1185 |
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+ | 0.6733 | 50.0 | 750 | 0.7148 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6964 | 51.0 | 765 | 0.7147 | 0.1153 | 0.0306 | 0.0638 | 0.1362 | 0.1285 |
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+ | 0.691 | 52.0 | 780 | 0.7143 | 0.1153 | 0.0306 | 0.0638 | 0.1362 | 0.1285 |
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+ | 0.6811 | 53.0 | 795 | 0.7139 | 0.1153 | 0.0306 | 0.0638 | 0.1362 | 0.1285 |
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+ | 0.6519 | 54.0 | 810 | 0.7135 | 0.1153 | 0.0306 | 0.0638 | 0.1362 | 0.1285 |
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+ | 0.6752 | 55.0 | 825 | 0.7133 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6827 | 56.0 | 840 | 0.7131 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6807 | 57.0 | 855 | 0.7128 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6722 | 58.0 | 870 | 0.7126 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6913 | 59.0 | 885 | 0.7125 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6672 | 60.0 | 900 | 0.7125 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6873 | 61.0 | 915 | 0.7125 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6704 | 62.0 | 930 | 0.7125 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6751 | 63.0 | 945 | 0.7124 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+ | 0.6605 | 64.0 | 960 | 0.7124 | 0.1101 | 0.0290 | 0.0603 | 0.1283 | 0.1245 |
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+
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+
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+ ### Framework versions
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+ - Transformers 4.31.0
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+ - Pytorch 2.0.1
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+ - Datasets 2.14.4
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+ - Tokenizers 0.13.3