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codet5p-770m-py-sanitized-codebleu-1-True-0.0001-0.1-lora-layer_16_17_18_19

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- library_name: peft
 
 
 
 
 
 
 
 
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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-0.0001-0.1-lora-layer_16_17_18_19
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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-0.0001-0.1-lora-layer_16_17_18_19
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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: 0.7418
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+ - Codebleu: 0.1258
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+ - Ngram Match Score: 0.0352
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+ - Weighted Ngram Match Score: 0.0679
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+ - Syntax Match Score: 0.1481
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+ - Dataflow Match Score: 0.1406
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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: 0.0001
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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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+ | 0.9818 | 1.0 | 15 | 0.9219 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.9717 | 2.0 | 30 | 0.9098 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 |
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+ | 0.9428 | 3.0 | 45 | 0.8798 | 0.0533 | 0.0025 | 0.0270 | 0.0595 | 0.0663 |
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+ | 0.8865 | 4.0 | 60 | 0.8411 | 0.1000 | 0.0226 | 0.0518 | 0.1270 | 0.1044 |
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+ | 0.8473 | 5.0 | 75 | 0.8122 | 0.1023 | 0.0213 | 0.0493 | 0.1296 | 0.1084 |
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+ | 0.8145 | 6.0 | 90 | 0.7809 | 0.0962 | 0.0116 | 0.0322 | 0.1151 | 0.1145 |
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+ | 0.7814 | 7.0 | 105 | 0.7522 | 0.0975 | 0.0143 | 0.0395 | 0.1177 | 0.1124 |
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+ | 0.74 | 8.0 | 120 | 0.7388 | 0.0992 | 0.0177 | 0.0430 | 0.1243 | 0.1084 |
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+ | 0.7269 | 9.0 | 135 | 0.7272 | 0.1094 | 0.0240 | 0.0508 | 0.1362 | 0.1185 |
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+ | 0.6985 | 10.0 | 150 | 0.7177 | 0.1108 | 0.0245 | 0.0510 | 0.1336 | 0.1245 |
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+ | 0.6873 | 11.0 | 165 | 0.7093 | 0.1047 | 0.0220 | 0.0484 | 0.1217 | 0.1225 |
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+ | 0.6805 | 12.0 | 180 | 0.7041 | 0.1038 | 0.0225 | 0.0473 | 0.1257 | 0.1165 |
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+ | 0.6601 | 13.0 | 195 | 0.6994 | 0.1040 | 0.0250 | 0.0486 | 0.1190 | 0.1225 |
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+ | 0.6371 | 14.0 | 210 | 0.6959 | 0.1061 | 0.0241 | 0.0494 | 0.1204 | 0.1265 |
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+ | 0.6466 | 15.0 | 225 | 0.6919 | 0.1052 | 0.0250 | 0.0504 | 0.1138 | 0.1305 |
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+ | 0.6336 | 16.0 | 240 | 0.6916 | 0.1043 | 0.0240 | 0.0504 | 0.1177 | 0.1245 |
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+ | 0.6158 | 17.0 | 255 | 0.6930 | 0.1036 | 0.0246 | 0.0506 | 0.1257 | 0.1145 |
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+ | 0.5909 | 18.0 | 270 | 0.6930 | 0.1201 | 0.0327 | 0.0608 | 0.1402 | 0.1365 |
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+ | 0.5813 | 19.0 | 285 | 0.6951 | 0.1243 | 0.0354 | 0.0601 | 0.1362 | 0.1506 |
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+ | 0.5738 | 20.0 | 300 | 0.6992 | 0.1147 | 0.0320 | 0.0613 | 0.1270 | 0.1365 |
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+ | 0.5575 | 21.0 | 315 | 0.6974 | 0.1235 | 0.0304 | 0.0576 | 0.1362 | 0.1506 |
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+ | 0.5492 | 22.0 | 330 | 0.6949 | 0.1159 | 0.0324 | 0.0595 | 0.1323 | 0.1345 |
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+ | 0.5373 | 23.0 | 345 | 0.6988 | 0.1075 | 0.0259 | 0.0509 | 0.1230 | 0.1265 |
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+ | 0.5311 | 24.0 | 360 | 0.7014 | 0.1105 | 0.0234 | 0.0460 | 0.1204 | 0.1386 |
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+ | 0.526 | 25.0 | 375 | 0.7044 | 0.1179 | 0.0294 | 0.0579 | 0.1362 | 0.1365 |
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+ | 0.5157 | 26.0 | 390 | 0.7028 | 0.1145 | 0.0262 | 0.0485 | 0.1190 | 0.1486 |
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+ | 0.4857 | 27.0 | 405 | 0.7099 | 0.1164 | 0.0247 | 0.0477 | 0.1283 | 0.1446 |
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+ | 0.5079 | 28.0 | 420 | 0.7047 | 0.1167 | 0.0260 | 0.0501 | 0.1323 | 0.1406 |
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+ | 0.4826 | 29.0 | 435 | 0.7073 | 0.1208 | 0.0260 | 0.0511 | 0.1362 | 0.1466 |
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+ | 0.4787 | 30.0 | 450 | 0.7149 | 0.1179 | 0.0290 | 0.0558 | 0.1230 | 0.1506 |
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+ | 0.4698 | 31.0 | 465 | 0.7115 | 0.1198 | 0.0269 | 0.0506 | 0.1336 | 0.1466 |
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+ | 0.4583 | 32.0 | 480 | 0.7158 | 0.1158 | 0.0298 | 0.0557 | 0.1296 | 0.1386 |
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+ | 0.462 | 33.0 | 495 | 0.7108 | 0.1175 | 0.0326 | 0.0622 | 0.1415 | 0.1285 |
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+ | 0.456 | 34.0 | 510 | 0.7176 | 0.1181 | 0.0297 | 0.0574 | 0.1310 | 0.1426 |
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+ | 0.4385 | 35.0 | 525 | 0.7214 | 0.1146 | 0.0260 | 0.0503 | 0.1310 | 0.1365 |
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+ | 0.4591 | 36.0 | 540 | 0.7117 | 0.1089 | 0.0254 | 0.0490 | 0.1190 | 0.1345 |
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+ | 0.4391 | 37.0 | 555 | 0.7167 | 0.1169 | 0.0265 | 0.0510 | 0.1283 | 0.1446 |
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+ | 0.439 | 38.0 | 570 | 0.7208 | 0.1122 | 0.0257 | 0.0493 | 0.1151 | 0.1466 |
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+ | 0.4344 | 39.0 | 585 | 0.7244 | 0.1185 | 0.0264 | 0.0509 | 0.1362 | 0.1406 |
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+ | 0.4412 | 40.0 | 600 | 0.7225 | 0.1179 | 0.0342 | 0.0619 | 0.1323 | 0.1386 |
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+ | 0.427 | 41.0 | 615 | 0.7268 | 0.1217 | 0.0329 | 0.0635 | 0.1455 | 0.1345 |
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+ | 0.4132 | 42.0 | 630 | 0.7292 | 0.1331 | 0.0310 | 0.0629 | 0.1627 | 0.1466 |
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+ | 0.4228 | 43.0 | 645 | 0.7293 | 0.1214 | 0.0298 | 0.0585 | 0.1429 | 0.1386 |
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+ | 0.4269 | 44.0 | 660 | 0.7295 | 0.1252 | 0.0297 | 0.0573 | 0.1508 | 0.1406 |
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+ | 0.4132 | 45.0 | 675 | 0.7320 | 0.1229 | 0.0315 | 0.0587 | 0.1442 | 0.1406 |
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+ | 0.4084 | 46.0 | 690 | 0.7343 | 0.1207 | 0.0348 | 0.0650 | 0.1442 | 0.1325 |
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+ | 0.4147 | 47.0 | 705 | 0.7298 | 0.1239 | 0.0302 | 0.0570 | 0.1455 | 0.1426 |
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+ | 0.4027 | 48.0 | 720 | 0.7295 | 0.1169 | 0.0300 | 0.0589 | 0.1415 | 0.1285 |
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+ | 0.4116 | 49.0 | 735 | 0.7348 | 0.1146 | 0.0317 | 0.0603 | 0.1310 | 0.1325 |
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+ | 0.4005 | 50.0 | 750 | 0.7387 | 0.1166 | 0.0303 | 0.0607 | 0.1402 | 0.1285 |
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+ | 0.4018 | 51.0 | 765 | 0.7337 | 0.1201 | 0.0314 | 0.0601 | 0.1389 | 0.1386 |
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+ | 0.4044 | 52.0 | 780 | 0.7343 | 0.1199 | 0.0323 | 0.0628 | 0.1415 | 0.1345 |
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+ | 0.4007 | 53.0 | 795 | 0.7392 | 0.1200 | 0.0325 | 0.0630 | 0.1336 | 0.1426 |
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+ | 0.3937 | 54.0 | 810 | 0.7392 | 0.1176 | 0.0321 | 0.0632 | 0.1336 | 0.1365 |
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+ | 0.4059 | 55.0 | 825 | 0.7366 | 0.1208 | 0.0332 | 0.0622 | 0.1455 | 0.1325 |
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+ | 0.3972 | 56.0 | 840 | 0.7363 | 0.1203 | 0.0319 | 0.0619 | 0.1429 | 0.1345 |
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+ | 0.3889 | 57.0 | 855 | 0.7399 | 0.1213 | 0.0301 | 0.0602 | 0.1402 | 0.1406 |
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+ | 0.3895 | 58.0 | 870 | 0.7410 | 0.1168 | 0.0313 | 0.0618 | 0.1362 | 0.1325 |
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+ | 0.3997 | 59.0 | 885 | 0.7411 | 0.1169 | 0.0321 | 0.0622 | 0.1362 | 0.1325 |
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+ | 0.3821 | 60.0 | 900 | 0.7409 | 0.1182 | 0.0305 | 0.0609 | 0.1402 | 0.1325 |
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+ | 0.3943 | 61.0 | 915 | 0.7412 | 0.1268 | 0.0351 | 0.0680 | 0.1508 | 0.1406 |
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+ | 0.3794 | 62.0 | 930 | 0.7416 | 0.1268 | 0.0351 | 0.0680 | 0.1508 | 0.1406 |
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+ | 0.3962 | 63.0 | 945 | 0.7419 | 0.1258 | 0.0352 | 0.0679 | 0.1481 | 0.1406 |
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+ | 0.3771 | 64.0 | 960 | 0.7418 | 0.1258 | 0.0352 | 0.0679 | 0.1481 | 0.1406 |
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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