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--- |
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license: bsd-3-clause |
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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-codebleu-1-True-1e-07-0.1 |
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results: [] |
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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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# codet5p-770m-py-codebleu-1-True-1e-07-0.1 |
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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: 0.6263 |
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- Codebleu: 0.0880 |
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- Ngram Match Score: 0.0119 |
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- Weighted Ngram Match Score: 0.0435 |
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- Syntax Match Score: 0.1209 |
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- Dataflow Match Score: 0.0852 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-07 |
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- train_batch_size: 6 |
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- eval_batch_size: 6 |
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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: 50 |
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- mixed_precision_training: Native AMP |
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### Training results |
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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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| 0.9753 | 1.0 | 63 | 0.9060 | 0.0244 | 0.0000 | 0.0108 | 0.0289 | 0.0293 | |
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| 0.9732 | 2.0 | 126 | 0.8664 | 0.0781 | 0.0104 | 0.0358 | 0.1089 | 0.0747 | |
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| 0.9044 | 3.0 | 189 | 0.8430 | 0.0802 | 0.0110 | 0.0363 | 0.1132 | 0.0754 | |
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| 0.8564 | 4.0 | 252 | 0.8162 | 0.0821 | 0.0116 | 0.0384 | 0.1146 | 0.0782 | |
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| 0.8289 | 5.0 | 315 | 0.7880 | 0.0861 | 0.0135 | 0.0430 | 0.1214 | 0.0796 | |
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| 0.8171 | 6.0 | 378 | 0.7615 | 0.0862 | 0.0134 | 0.0422 | 0.1219 | 0.0796 | |
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| 0.7935 | 7.0 | 441 | 0.7390 | 0.0856 | 0.0136 | 0.0423 | 0.1204 | 0.0796 | |
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| 0.781 | 8.0 | 504 | 0.7206 | 0.0883 | 0.0143 | 0.0435 | 0.1219 | 0.0845 | |
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| 0.7608 | 9.0 | 567 | 0.7065 | 0.0855 | 0.0122 | 0.0396 | 0.1171 | 0.0838 | |
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| 0.7404 | 10.0 | 630 | 0.6934 | 0.0804 | 0.0094 | 0.0351 | 0.1118 | 0.0782 | |
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| 0.7388 | 11.0 | 693 | 0.6847 | 0.0787 | 0.0089 | 0.0337 | 0.1108 | 0.0754 | |
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| 0.7178 | 12.0 | 756 | 0.6786 | 0.0792 | 0.0090 | 0.0339 | 0.1113 | 0.0761 | |
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| 0.7087 | 13.0 | 819 | 0.6736 | 0.0811 | 0.0106 | 0.0388 | 0.1122 | 0.0782 | |
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| 0.7035 | 14.0 | 882 | 0.6690 | 0.0820 | 0.0109 | 0.0388 | 0.1122 | 0.0803 | |
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| 0.7005 | 15.0 | 945 | 0.6652 | 0.0842 | 0.0106 | 0.0384 | 0.1151 | 0.0831 | |
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| 0.688 | 16.0 | 1008 | 0.6620 | 0.0835 | 0.0104 | 0.0380 | 0.1156 | 0.0810 | |
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| 0.6911 | 17.0 | 1071 | 0.6587 | 0.0833 | 0.0106 | 0.0382 | 0.1166 | 0.0796 | |
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| 0.6782 | 18.0 | 1134 | 0.6559 | 0.0851 | 0.0114 | 0.0416 | 0.1156 | 0.0838 | |
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| 0.678 | 19.0 | 1197 | 0.6536 | 0.0844 | 0.0115 | 0.0416 | 0.1132 | 0.0845 | |
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| 0.6657 | 20.0 | 1260 | 0.6512 | 0.0856 | 0.0118 | 0.0422 | 0.1132 | 0.0873 | |
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| 0.6702 | 21.0 | 1323 | 0.6491 | 0.0842 | 0.0115 | 0.0416 | 0.1113 | 0.0859 | |
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| 0.662 | 22.0 | 1386 | 0.6471 | 0.0842 | 0.0115 | 0.0416 | 0.1113 | 0.0859 | |
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| 0.6569 | 23.0 | 1449 | 0.6453 | 0.0842 | 0.0116 | 0.0416 | 0.1113 | 0.0859 | |
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| 0.6605 | 24.0 | 1512 | 0.6436 | 0.0860 | 0.0114 | 0.0424 | 0.1171 | 0.0845 | |
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| 0.6589 | 25.0 | 1575 | 0.6420 | 0.0860 | 0.0114 | 0.0424 | 0.1171 | 0.0845 | |
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| 0.6519 | 26.0 | 1638 | 0.6404 | 0.0874 | 0.0118 | 0.0429 | 0.1190 | 0.0859 | |
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| 0.6568 | 27.0 | 1701 | 0.6390 | 0.0874 | 0.0118 | 0.0429 | 0.1190 | 0.0859 | |
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| 0.6569 | 28.0 | 1764 | 0.6378 | 0.0874 | 0.0116 | 0.0428 | 0.1190 | 0.0859 | |
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| 0.6455 | 29.0 | 1827 | 0.6365 | 0.0874 | 0.0116 | 0.0428 | 0.1190 | 0.0859 | |
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| 0.6456 | 30.0 | 1890 | 0.6355 | 0.0880 | 0.0116 | 0.0428 | 0.1190 | 0.0873 | |
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| 0.6503 | 31.0 | 1953 | 0.6345 | 0.0880 | 0.0116 | 0.0428 | 0.1190 | 0.0873 | |
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| 0.6424 | 32.0 | 2016 | 0.6337 | 0.0880 | 0.0116 | 0.0428 | 0.1190 | 0.0873 | |
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| 0.644 | 33.0 | 2079 | 0.6328 | 0.0880 | 0.0116 | 0.0428 | 0.1190 | 0.0873 | |
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| 0.6429 | 34.0 | 2142 | 0.6320 | 0.0872 | 0.0120 | 0.0435 | 0.1190 | 0.0852 | |
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| 0.6436 | 35.0 | 2205 | 0.6313 | 0.0872 | 0.0120 | 0.0435 | 0.1190 | 0.0852 | |
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| 0.638 | 36.0 | 2268 | 0.6307 | 0.0872 | 0.0120 | 0.0435 | 0.1190 | 0.0852 | |
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| 0.6381 | 37.0 | 2331 | 0.6300 | 0.0872 | 0.0120 | 0.0435 | 0.1190 | 0.0852 | |
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| 0.6307 | 38.0 | 2394 | 0.6295 | 0.0872 | 0.0120 | 0.0435 | 0.1190 | 0.0852 | |
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| 0.6344 | 39.0 | 2457 | 0.6289 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.6296 | 40.0 | 2520 | 0.6285 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.6268 | 41.0 | 2583 | 0.6280 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.6315 | 42.0 | 2646 | 0.6276 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.6265 | 43.0 | 2709 | 0.6273 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.626 | 44.0 | 2772 | 0.6270 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.631 | 45.0 | 2835 | 0.6268 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.6315 | 46.0 | 2898 | 0.6266 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.6309 | 47.0 | 2961 | 0.6264 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.627 | 48.0 | 3024 | 0.6263 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.6252 | 49.0 | 3087 | 0.6262 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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| 0.632 | 50.0 | 3150 | 0.6263 | 0.0880 | 0.0119 | 0.0435 | 0.1209 | 0.0852 | |
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### Framework versions |
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- Transformers 4.30.0.dev0 |
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- Pytorch 2.0.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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