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codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_22

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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_22
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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_22
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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: 0.7042
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+ - Codebleu: 0.1212
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+ - Ngram Match Score: 0.0274
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+ - Weighted Ngram Match Score: 0.0565
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+ - Syntax Match Score: 0.1495
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+ - Dataflow Match Score: 0.1325
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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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+ | 0.9723 | 1.0 | 15 | 0.9246 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.9639 | 2.0 | 30 | 0.9234 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.9718 | 3.0 | 45 | 0.9206 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.9428 | 4.0 | 60 | 0.9142 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 |
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+ | 0.9587 | 5.0 | 75 | 0.9003 | 0.0268 | 0.0000 | 0.0118 | 0.0238 | 0.0402 |
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+ | 0.9449 | 6.0 | 90 | 0.8766 | 0.0564 | 0.0022 | 0.0266 | 0.0635 | 0.0703 |
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+ | 0.9286 | 7.0 | 105 | 0.8498 | 0.0988 | 0.0260 | 0.0581 | 0.1257 | 0.1004 |
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+ | 0.8737 | 8.0 | 120 | 0.8287 | 0.0962 | 0.0243 | 0.0517 | 0.1230 | 0.0984 |
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+ | 0.8563 | 9.0 | 135 | 0.8129 | 0.0913 | 0.0219 | 0.0477 | 0.1164 | 0.0944 |
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+ | 0.8353 | 10.0 | 150 | 0.7987 | 0.0906 | 0.0218 | 0.0470 | 0.1190 | 0.0904 |
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+ | 0.8101 | 11.0 | 165 | 0.7838 | 0.0961 | 0.0255 | 0.0557 | 0.1257 | 0.0944 |
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+ | 0.806 | 12.0 | 180 | 0.7687 | 0.1089 | 0.0248 | 0.0585 | 0.1349 | 0.1165 |
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+ | 0.7869 | 13.0 | 195 | 0.7588 | 0.1070 | 0.0245 | 0.0585 | 0.1323 | 0.1145 |
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+ | 0.7652 | 14.0 | 210 | 0.7518 | 0.1092 | 0.0244 | 0.0595 | 0.1336 | 0.1185 |
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+ | 0.7805 | 15.0 | 225 | 0.7472 | 0.1087 | 0.0256 | 0.0609 | 0.1336 | 0.1165 |
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+ | 0.7846 | 16.0 | 240 | 0.7444 | 0.1086 | 0.0292 | 0.0642 | 0.1336 | 0.1145 |
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+ | 0.7664 | 17.0 | 255 | 0.7423 | 0.1157 | 0.0281 | 0.0590 | 0.1429 | 0.1245 |
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+ | 0.7486 | 18.0 | 270 | 0.7400 | 0.1112 | 0.0259 | 0.0571 | 0.1389 | 0.1185 |
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+ | 0.7587 | 19.0 | 285 | 0.7376 | 0.1147 | 0.0259 | 0.0570 | 0.1415 | 0.1245 |
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+ | 0.7496 | 20.0 | 300 | 0.7356 | 0.1169 | 0.0266 | 0.0571 | 0.1429 | 0.1285 |
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+ | 0.736 | 21.0 | 315 | 0.7327 | 0.1151 | 0.0263 | 0.0553 | 0.1429 | 0.1245 |
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+ | 0.7302 | 22.0 | 330 | 0.7302 | 0.1172 | 0.0265 | 0.0551 | 0.1442 | 0.1285 |
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+ | 0.7346 | 23.0 | 345 | 0.7293 | 0.1143 | 0.0264 | 0.0550 | 0.1389 | 0.1265 |
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+ | 0.7345 | 24.0 | 360 | 0.7277 | 0.1143 | 0.0264 | 0.0550 | 0.1389 | 0.1265 |
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+ | 0.7519 | 25.0 | 375 | 0.7257 | 0.1143 | 0.0267 | 0.0549 | 0.1389 | 0.1265 |
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+ | 0.7312 | 26.0 | 390 | 0.7243 | 0.1121 | 0.0264 | 0.0549 | 0.1376 | 0.1225 |
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+ | 0.7171 | 27.0 | 405 | 0.7226 | 0.1143 | 0.0266 | 0.0547 | 0.1389 | 0.1265 |
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+ | 0.7245 | 28.0 | 420 | 0.7215 | 0.1122 | 0.0263 | 0.0550 | 0.1376 | 0.1225 |
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+ | 0.7097 | 29.0 | 435 | 0.7205 | 0.1148 | 0.0264 | 0.0548 | 0.1402 | 0.1265 |
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+ | 0.713 | 30.0 | 450 | 0.7196 | 0.1127 | 0.0262 | 0.0550 | 0.1389 | 0.1225 |
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+ | 0.705 | 31.0 | 465 | 0.7191 | 0.1120 | 0.0266 | 0.0555 | 0.1389 | 0.1205 |
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+ | 0.7059 | 32.0 | 480 | 0.7182 | 0.1120 | 0.0266 | 0.0555 | 0.1389 | 0.1205 |
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+ | 0.7122 | 33.0 | 495 | 0.7170 | 0.1120 | 0.0266 | 0.0555 | 0.1389 | 0.1205 |
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+ | 0.7114 | 34.0 | 510 | 0.7152 | 0.1120 | 0.0266 | 0.0556 | 0.1389 | 0.1205 |
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+ | 0.6947 | 35.0 | 525 | 0.7142 | 0.1120 | 0.0267 | 0.0555 | 0.1389 | 0.1205 |
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+ | 0.7165 | 36.0 | 540 | 0.7134 | 0.1158 | 0.0269 | 0.0560 | 0.1442 | 0.1245 |
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+ | 0.7029 | 37.0 | 555 | 0.7131 | 0.1158 | 0.0273 | 0.0560 | 0.1442 | 0.1245 |
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+ | 0.7117 | 38.0 | 570 | 0.7126 | 0.1155 | 0.0269 | 0.0561 | 0.1455 | 0.1225 |
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+ | 0.7037 | 39.0 | 585 | 0.7117 | 0.1174 | 0.0275 | 0.0560 | 0.1481 | 0.1245 |
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+ | 0.7089 | 40.0 | 600 | 0.7113 | 0.1220 | 0.0278 | 0.0557 | 0.1495 | 0.1345 |
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+ | 0.6944 | 41.0 | 615 | 0.7107 | 0.1200 | 0.0272 | 0.0559 | 0.1468 | 0.1325 |
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+ | 0.6907 | 42.0 | 630 | 0.7101 | 0.1200 | 0.0272 | 0.0559 | 0.1468 | 0.1325 |
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+ | 0.691 | 43.0 | 645 | 0.7097 | 0.1233 | 0.0275 | 0.0563 | 0.1548 | 0.1325 |
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+ | 0.6967 | 44.0 | 660 | 0.7091 | 0.1233 | 0.0275 | 0.0563 | 0.1548 | 0.1325 |
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+ | 0.6893 | 45.0 | 675 | 0.7087 | 0.1233 | 0.0275 | 0.0563 | 0.1548 | 0.1325 |
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+ | 0.6881 | 46.0 | 690 | 0.7086 | 0.1233 | 0.0275 | 0.0563 | 0.1548 | 0.1325 |
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+ | 0.6908 | 47.0 | 705 | 0.7081 | 0.1233 | 0.0276 | 0.0563 | 0.1548 | 0.1325 |
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+ | 0.6895 | 48.0 | 720 | 0.7077 | 0.1222 | 0.0274 | 0.0563 | 0.1521 | 0.1325 |
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+ | 0.6951 | 49.0 | 735 | 0.7072 | 0.1222 | 0.0274 | 0.0563 | 0.1521 | 0.1325 |
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+ | 0.6833 | 50.0 | 750 | 0.7069 | 0.1244 | 0.0279 | 0.0563 | 0.1534 | 0.1365 |
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+ | 0.709 | 51.0 | 765 | 0.7065 | 0.1244 | 0.0279 | 0.0563 | 0.1534 | 0.1365 |
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+ | 0.6997 | 52.0 | 780 | 0.7060 | 0.1238 | 0.0273 | 0.0563 | 0.1521 | 0.1365 |
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+ | 0.6982 | 53.0 | 795 | 0.7056 | 0.1217 | 0.0271 | 0.0565 | 0.1508 | 0.1325 |
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+ | 0.6658 | 54.0 | 810 | 0.7053 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.6897 | 55.0 | 825 | 0.7050 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.6889 | 56.0 | 840 | 0.7049 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.693 | 57.0 | 855 | 0.7047 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.6859 | 58.0 | 870 | 0.7045 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.6962 | 59.0 | 885 | 0.7044 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.6827 | 60.0 | 900 | 0.7043 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.695 | 61.0 | 915 | 0.7043 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.681 | 62.0 | 930 | 0.7042 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.6854 | 63.0 | 945 | 0.7042 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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+ | 0.6778 | 64.0 | 960 | 0.7042 | 0.1212 | 0.0274 | 0.0565 | 0.1495 | 0.1325 |
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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