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codet5p-770m-py-sanitized-codebleu-1-True-5e-05-0.1-lora-layer_14

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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_14
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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_14
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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: 0.7195
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+ - Codebleu: 0.1029
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+ - Ngram Match Score: 0.0205
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+ - Weighted Ngram Match Score: 0.0424
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+ - Syntax Match Score: 0.1230
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+ - Dataflow Match Score: 0.1185
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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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+ | 0.9766 | 1.0 | 15 | 0.9244 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.965 | 2.0 | 30 | 0.9226 | 0.0072 | 0.0000 | 0.0000 | 0.0079 | 0.0100 |
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+ | 0.9762 | 3.0 | 45 | 0.9181 | 0.0080 | 0.0000 | 0.0000 | 0.0079 | 0.0120 |
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+ | 0.9451 | 4.0 | 60 | 0.9083 | 0.0096 | 0.0000 | 0.0001 | 0.0079 | 0.0161 |
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+ | 0.9517 | 5.0 | 75 | 0.8892 | 0.0444 | 0.0014 | 0.0239 | 0.0423 | 0.0622 |
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+ | 0.9375 | 6.0 | 90 | 0.8647 | 0.0894 | 0.0218 | 0.0480 | 0.1098 | 0.0964 |
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+ | 0.9163 | 7.0 | 105 | 0.8474 | 0.0990 | 0.0226 | 0.0521 | 0.1243 | 0.1044 |
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+ | 0.8645 | 8.0 | 120 | 0.8333 | 0.0991 | 0.0239 | 0.0522 | 0.1243 | 0.1044 |
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+ | 0.855 | 9.0 | 135 | 0.8221 | 0.0996 | 0.0169 | 0.0398 | 0.1243 | 0.1104 |
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+ | 0.8493 | 10.0 | 150 | 0.8122 | 0.1011 | 0.0182 | 0.0428 | 0.1270 | 0.1104 |
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+ | 0.8388 | 11.0 | 165 | 0.8030 | 0.1054 | 0.0158 | 0.0403 | 0.1270 | 0.1225 |
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+ | 0.8374 | 12.0 | 180 | 0.7955 | 0.1073 | 0.0165 | 0.0450 | 0.1283 | 0.1245 |
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+ | 0.816 | 13.0 | 195 | 0.7893 | 0.1092 | 0.0161 | 0.0436 | 0.1296 | 0.1285 |
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+ | 0.7974 | 14.0 | 210 | 0.7820 | 0.1092 | 0.0158 | 0.0436 | 0.1296 | 0.1285 |
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+ | 0.804 | 15.0 | 225 | 0.7744 | 0.1080 | 0.0165 | 0.0439 | 0.1283 | 0.1265 |
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+ | 0.8055 | 16.0 | 240 | 0.7697 | 0.1005 | 0.0155 | 0.0417 | 0.1204 | 0.1165 |
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+ | 0.7875 | 17.0 | 255 | 0.7646 | 0.1021 | 0.0155 | 0.0418 | 0.1204 | 0.1205 |
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+ | 0.7592 | 18.0 | 270 | 0.7602 | 0.1060 | 0.0170 | 0.0421 | 0.1217 | 0.1285 |
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+ | 0.7714 | 19.0 | 285 | 0.7550 | 0.1037 | 0.0170 | 0.0431 | 0.1217 | 0.1225 |
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+ | 0.7613 | 20.0 | 300 | 0.7515 | 0.1080 | 0.0218 | 0.0496 | 0.1257 | 0.1265 |
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+ | 0.7469 | 21.0 | 315 | 0.7490 | 0.1042 | 0.0169 | 0.0372 | 0.1204 | 0.1265 |
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+ | 0.7363 | 22.0 | 330 | 0.7458 | 0.1045 | 0.0213 | 0.0439 | 0.1164 | 0.1285 |
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+ | 0.7404 | 23.0 | 345 | 0.7436 | 0.1060 | 0.0209 | 0.0437 | 0.1243 | 0.1245 |
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+ | 0.74 | 24.0 | 360 | 0.7411 | 0.1122 | 0.0240 | 0.0499 | 0.1296 | 0.1325 |
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+ | 0.7588 | 25.0 | 375 | 0.7380 | 0.1093 | 0.0239 | 0.0503 | 0.1283 | 0.1265 |
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+ | 0.7315 | 26.0 | 390 | 0.7370 | 0.1063 | 0.0221 | 0.0455 | 0.1283 | 0.1205 |
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+ | 0.7152 | 27.0 | 405 | 0.7342 | 0.1095 | 0.0257 | 0.0527 | 0.1296 | 0.1245 |
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+ | 0.7183 | 28.0 | 420 | 0.7335 | 0.1060 | 0.0236 | 0.0491 | 0.1243 | 0.1225 |
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+ | 0.7037 | 29.0 | 435 | 0.7327 | 0.1095 | 0.0256 | 0.0529 | 0.1296 | 0.1245 |
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+ | 0.706 | 30.0 | 450 | 0.7315 | 0.1108 | 0.0266 | 0.0544 | 0.1323 | 0.1245 |
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+ | 0.7004 | 31.0 | 465 | 0.7302 | 0.1131 | 0.0254 | 0.0541 | 0.1362 | 0.1265 |
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+ | 0.7049 | 32.0 | 480 | 0.7294 | 0.1083 | 0.0233 | 0.0489 | 0.1283 | 0.1245 |
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+ | 0.6944 | 33.0 | 495 | 0.7295 | 0.1101 | 0.0230 | 0.0483 | 0.1310 | 0.1265 |
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+ | 0.7039 | 34.0 | 510 | 0.7283 | 0.1023 | 0.0197 | 0.0403 | 0.1204 | 0.1205 |
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+ | 0.6837 | 35.0 | 525 | 0.7275 | 0.1014 | 0.0191 | 0.0400 | 0.1204 | 0.1185 |
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+ | 0.706 | 36.0 | 540 | 0.7270 | 0.1023 | 0.0197 | 0.0403 | 0.1204 | 0.1205 |
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+ | 0.6997 | 37.0 | 555 | 0.7267 | 0.1005 | 0.0199 | 0.0403 | 0.1177 | 0.1185 |
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+ | 0.7007 | 38.0 | 570 | 0.7268 | 0.1026 | 0.0210 | 0.0419 | 0.1204 | 0.1205 |
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+ | 0.6893 | 39.0 | 585 | 0.7252 | 0.1055 | 0.0242 | 0.0488 | 0.1230 | 0.1225 |
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+ | 0.7017 | 40.0 | 600 | 0.7242 | 0.1005 | 0.0199 | 0.0403 | 0.1177 | 0.1185 |
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+ | 0.6847 | 41.0 | 615 | 0.7238 | 0.1083 | 0.0257 | 0.0515 | 0.1230 | 0.1285 |
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+ | 0.6803 | 42.0 | 630 | 0.7233 | 0.1020 | 0.0207 | 0.0414 | 0.1190 | 0.1205 |
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+ | 0.6827 | 43.0 | 645 | 0.7232 | 0.1078 | 0.0268 | 0.0531 | 0.1230 | 0.1265 |
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+ | 0.6888 | 44.0 | 660 | 0.7230 | 0.1000 | 0.0213 | 0.0419 | 0.1177 | 0.1165 |
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+ | 0.6792 | 45.0 | 675 | 0.7229 | 0.1000 | 0.0213 | 0.0419 | 0.1177 | 0.1165 |
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+ | 0.6761 | 46.0 | 690 | 0.7230 | 0.1005 | 0.0204 | 0.0403 | 0.1177 | 0.1185 |
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+ | 0.6844 | 47.0 | 705 | 0.7226 | 0.1013 | 0.0202 | 0.0403 | 0.1177 | 0.1205 |
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+ | 0.6799 | 48.0 | 720 | 0.7226 | 0.1008 | 0.0212 | 0.0419 | 0.1177 | 0.1185 |
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+ | 0.6884 | 49.0 | 735 | 0.7218 | 0.1008 | 0.0212 | 0.0419 | 0.1177 | 0.1185 |
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+ | 0.6694 | 50.0 | 750 | 0.7216 | 0.1034 | 0.0196 | 0.0403 | 0.1230 | 0.1205 |
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+ | 0.6934 | 51.0 | 765 | 0.7214 | 0.1112 | 0.0254 | 0.0515 | 0.1283 | 0.1305 |
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+ | 0.6902 | 52.0 | 780 | 0.7209 | 0.1008 | 0.0211 | 0.0424 | 0.1177 | 0.1185 |
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+ | 0.6845 | 53.0 | 795 | 0.7206 | 0.1008 | 0.0211 | 0.0424 | 0.1177 | 0.1185 |
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+ | 0.6517 | 54.0 | 810 | 0.7206 | 0.1008 | 0.0211 | 0.0424 | 0.1177 | 0.1185 |
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+ | 0.6741 | 55.0 | 825 | 0.7204 | 0.0978 | 0.0218 | 0.0433 | 0.1177 | 0.1104 |
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+ | 0.6805 | 56.0 | 840 | 0.7202 | 0.0978 | 0.0218 | 0.0433 | 0.1177 | 0.1104 |
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+ | 0.6829 | 57.0 | 855 | 0.7199 | 0.0978 | 0.0218 | 0.0433 | 0.1177 | 0.1104 |
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+ | 0.6693 | 58.0 | 870 | 0.7198 | 0.1029 | 0.0205 | 0.0424 | 0.1230 | 0.1185 |
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+ | 0.6898 | 59.0 | 885 | 0.7196 | 0.1029 | 0.0205 | 0.0424 | 0.1230 | 0.1185 |
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+ | 0.6658 | 60.0 | 900 | 0.7195 | 0.1029 | 0.0205 | 0.0424 | 0.1230 | 0.1185 |
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+ | 0.6818 | 61.0 | 915 | 0.7195 | 0.0978 | 0.0218 | 0.0433 | 0.1177 | 0.1104 |
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+ | 0.673 | 62.0 | 930 | 0.7195 | 0.1029 | 0.0205 | 0.0424 | 0.1230 | 0.1185 |
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+ | 0.6703 | 63.0 | 945 | 0.7195 | 0.1008 | 0.0211 | 0.0424 | 0.1177 | 0.1185 |
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+ | 0.6609 | 64.0 | 960 | 0.7195 | 0.1029 | 0.0205 | 0.0424 | 0.1230 | 0.1185 |
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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