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--- |
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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-1e-07-0.1-prefix-tuning |
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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-sanitized-codebleu-1-True-1e-07-0.1-prefix-tuning |
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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: 7.8250 |
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- Codebleu: 0.0215 |
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- Ngram Match Score: 0.0004 |
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- Weighted Ngram Match Score: 0.0003 |
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- Syntax Match Score: 0.0013 |
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- Dataflow Match Score: 0.0522 |
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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: 16 |
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- eval_batch_size: 16 |
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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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### 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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| 7.9988 | 1.0 | 8 | 7.8277 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 8.0077 | 2.0 | 16 | 7.8276 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 8.0173 | 3.0 | 24 | 7.8276 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 7.996 | 4.0 | 32 | 7.8276 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 8.0369 | 5.0 | 40 | 7.8276 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 8.0406 | 6.0 | 48 | 7.8276 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 8.0162 | 7.0 | 56 | 7.8275 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 7.9996 | 8.0 | 64 | 7.8274 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 7.9955 | 9.0 | 72 | 7.8274 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 8.015 | 10.0 | 80 | 7.8273 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 8.0264 | 11.0 | 88 | 7.8272 | 0.0231 | 0.0004 | 0.0004 | 0.0013 | 0.0562 | |
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| 8.0091 | 12.0 | 96 | 7.8270 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0184 | 13.0 | 104 | 7.8269 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0261 | 14.0 | 112 | 7.8268 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9791 | 15.0 | 120 | 7.8267 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0159 | 16.0 | 128 | 7.8265 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9996 | 17.0 | 136 | 7.8264 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0404 | 18.0 | 144 | 7.8263 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9689 | 19.0 | 152 | 7.8262 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0391 | 20.0 | 160 | 7.8261 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9905 | 21.0 | 168 | 7.8260 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0239 | 22.0 | 176 | 7.8259 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0125 | 23.0 | 184 | 7.8258 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9988 | 24.0 | 192 | 7.8257 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9675 | 25.0 | 200 | 7.8257 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0127 | 26.0 | 208 | 7.8256 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0141 | 27.0 | 216 | 7.8255 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0028 | 28.0 | 224 | 7.8255 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0304 | 29.0 | 232 | 7.8254 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0433 | 30.0 | 240 | 7.8254 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0184 | 31.0 | 248 | 7.8253 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0456 | 32.0 | 256 | 7.8253 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0378 | 33.0 | 264 | 7.8252 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0035 | 34.0 | 272 | 7.8252 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0212 | 35.0 | 280 | 7.8252 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0033 | 36.0 | 288 | 7.8251 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0311 | 37.0 | 296 | 7.8251 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0264 | 38.0 | 304 | 7.8251 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9892 | 39.0 | 312 | 7.8251 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0047 | 40.0 | 320 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0111 | 41.0 | 328 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0124 | 42.0 | 336 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0408 | 43.0 | 344 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9969 | 44.0 | 352 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0283 | 45.0 | 360 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0449 | 46.0 | 368 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0017 | 47.0 | 376 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0328 | 48.0 | 384 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 7.9923 | 49.0 | 392 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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| 8.0266 | 50.0 | 400 | 7.8250 | 0.0215 | 0.0004 | 0.0003 | 0.0013 | 0.0522 | |
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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 |
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