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README.md
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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-32-True-1e-06-0.1
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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-32-True-1e-06-0.1
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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: 0.8087
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- Codebleu: 0.0867
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- Ngram Match Score: 0.0137
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- Weighted Ngram Match Score: 0.0422
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- Syntax Match Score: 0.1204
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- Dataflow Match Score: 0.0824
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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-06
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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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- gradient_accumulation_steps: 32
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- total_train_batch_size: 192
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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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| 1.9228 | 0.51 | 1 | 0.9113 | 0.0047 | 0.0000 | 0.0000 | 0.0048 | 0.0070 |
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| 0.9857 | 1.52 | 3 | 0.9112 | 0.0047 | 0.0000 | 0.0000 | 0.0048 | 0.0070 |
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| 0.9734 | 2.54 | 5 | 0.9112 | 0.0069 | 0.0000 | 0.0001 | 0.0067 | 0.0105 |
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| 0.9624 | 3.56 | 7 | 0.9111 | 0.0074 | 0.0000 | 0.0002 | 0.0072 | 0.0112 |
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| 0.9586 | 4.57 | 9 | 0.9107 | 0.0087 | 0.0000 | 0.0003 | 0.0092 | 0.0126 |
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| 0.9708 | 5.59 | 11 | 0.9097 | 0.0140 | 0.0000 | 0.0019 | 0.0178 | 0.0168 |
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| 0.9667 | 6.6 | 13 | 0.9092 | 0.0171 | 0.0000 | 0.0034 | 0.0202 | 0.0216 |
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| 0.9791 | 7.62 | 15 | 0.9058 | 0.0211 | 0.0000 | 0.0057 | 0.0255 | 0.0258 |
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| 0.9702 | 8.63 | 17 | 0.9048 | 0.0317 | 0.0001 | 0.0144 | 0.0366 | 0.0391 |
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| 0.9563 | 9.65 | 19 | 0.9034 | 0.0398 | 0.0007 | 0.0192 | 0.0477 | 0.0468 |
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| 0.9654 | 10.67 | 21 | 0.8927 | 0.0482 | 0.0014 | 0.0215 | 0.0583 | 0.0566 |
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| 0.9458 | 11.68 | 23 | 0.8898 | 0.0602 | 0.0043 | 0.0275 | 0.0742 | 0.0684 |
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| 0.9523 | 12.7 | 25 | 0.8866 | 0.0647 | 0.0053 | 0.0286 | 0.0829 | 0.0705 |
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| 0.942 | 13.71 | 27 | 0.8847 | 0.0786 | 0.0091 | 0.0338 | 0.1069 | 0.0789 |
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| 0.94 | 14.73 | 29 | 0.8648 | 0.0798 | 0.0099 | 0.0357 | 0.1079 | 0.0803 |
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| 0.9025 | 15.75 | 31 | 0.8604 | 0.0809 | 0.0105 | 0.0363 | 0.1122 | 0.0782 |
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| 0.9058 | 16.76 | 33 | 0.8577 | 0.0815 | 0.0107 | 0.0362 | 0.1132 | 0.0789 |
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| 0.893 | 17.78 | 35 | 0.8543 | 0.0816 | 0.0110 | 0.0363 | 0.1132 | 0.0789 |
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| 0.8959 | 18.79 | 37 | 0.8524 | 0.0805 | 0.0109 | 0.0362 | 0.1113 | 0.0782 |
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| 0.877 | 19.81 | 39 | 0.8422 | 0.0808 | 0.0118 | 0.0385 | 0.1113 | 0.0782 |
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| 0.861 | 20.83 | 41 | 0.8374 | 0.0811 | 0.0118 | 0.0385 | 0.1113 | 0.0789 |
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| 0.8365 | 21.84 | 43 | 0.8376 | 0.0827 | 0.0119 | 0.0386 | 0.1132 | 0.0810 |
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| 0.8293 | 22.86 | 45 | 0.8331 | 0.0853 | 0.0126 | 0.0390 | 0.1180 | 0.0824 |
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| 0.8288 | 23.87 | 47 | 0.8246 | 0.0852 | 0.0134 | 0.0421 | 0.1180 | 0.0810 |
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| 0.8175 | 24.89 | 49 | 0.8141 | 0.0852 | 0.0134 | 0.0421 | 0.1180 | 0.0810 |
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| 0.6345 | 25.4 | 50 | 0.8087 | 0.0867 | 0.0137 | 0.0422 | 0.1204 | 0.0824 |
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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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