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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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metrics: |
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- bleu |
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model-index: |
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- name: CommitPredictorT5 |
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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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# CommitPredictorT5 |
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This model is a fine-tuned version of [Salesforce/codet5-base-multi-sum](https://huggingface.co/Salesforce/codet5-base-multi-sum) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.4669 |
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- Bleu: 0.0002 |
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- Precisions: [0.003189792663476874, 0.00016826518593303046, 0.000321853878339234, 0.0036900369003690036] |
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- Brevity Penalty: 0.2394 |
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- Length Ratio: 0.4116 |
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- Translation Length: 10658 |
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- Reference Length: 25896 |
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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: 2e-05 |
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- train_batch_size: 42 |
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- eval_batch_size: 42 |
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- seed: 42 |
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- gradient_accumulation_steps: 3 |
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- total_train_batch_size: 126 |
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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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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:| |
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| No log | 1.0 | 299 | 2.8109 | 0.0002 | [0.003640040444893832, 0.00019327406262079628, 0.0003745318352059925, 0.006024096385542169] | 0.1982 | 0.3819 | 9889 | 25896 | |
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| 3.1102 | 2.0 | 598 | 2.6662 | 0.0002 | [0.004371150407311742, 0.00018691588785046728, 0.00036114120621162876, 0.005319148936170213] | 0.2074 | 0.3887 | 10065 | 25896 | |
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| 3.1102 | 3.0 | 897 | 2.5869 | 0.0002 | [0.0033418517790446234, 0.00018321729571271528, 0.0003546099290780142, 0.005494505494505495] | 0.2132 | 0.3928 | 10173 | 25896 | |
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| 2.6696 | 4.0 | 1196 | 2.5371 | 0.0002 | [0.0033398821218074658, 0.00018301610541727673, 0.0003522367030644593, 0.004672897196261682] | 0.2135 | 0.3931 | 10179 | 25896 | |
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| 2.6696 | 5.0 | 1495 | 2.5077 | 0.0002 | [0.003243655790879603, 0.0001734304543877905, 0.0003356831151393085, 0.005208333333333333] | 0.2298 | 0.4047 | 10481 | 25896 | |
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| 2.4738 | 6.0 | 1794 | 2.4810 | 0.0002 | [0.0029016345874842827, 0.00017784101013693757, 0.00034234851078397807, 0.0045662100456621] | 0.2220 | 0.3992 | 10338 | 25896 | |
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| 2.3139 | 7.0 | 2093 | 2.4625 | 0.0002 | [0.002756130013305455, 0.0001722356183258698, 0.00033101621979476995, 0.00423728813559322] | 0.2319 | 0.4063 | 10521 | 25896 | |
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| 2.3139 | 8.0 | 2392 | 2.4556 | 0.0002 | [0.0027348170501697473, 0.00016983695652173913, 0.0003266906239790918, 0.004273504273504274] | 0.2364 | 0.4094 | 10603 | 25896 | |
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| 2.1842 | 9.0 | 2691 | 2.4470 | 0.0002 | [0.003198193961057285, 0.000169061707523246, 0.00032658393207054214, 0.004784688995215311] | 0.2378 | 0.4105 | 10630 | 25896 | |
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| 2.1842 | 10.0 | 2990 | 2.4439 | 0.0002 | [0.0033203680865193054, 0.00017167381974248928, 0.000328515111695138, 0.0038022813688212928] | 0.2330 | 0.4070 | 10540 | 25896 | |
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| 2.0831 | 11.0 | 3289 | 2.4435 | 0.0002 | [0.0032796101949025486, 0.000167897918065816, 0.000321853878339234, 0.003875968992248062] | 0.2401 | 0.4121 | 10671 | 25896 | |
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| 1.9685 | 12.0 | 3588 | 2.4483 | 0.0002 | [0.0037652056381540836, 0.0001772421127259837, 0.0003397893306150187, 0.004098360655737705] | 0.2231 | 0.3999 | 10357 | 25896 | |
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| 1.9685 | 13.0 | 3887 | 2.4557 | 0.0002 | [0.0033178500331785005, 0.00017143836790673754, 0.000327653997378768, 0.0036900369003690036] | 0.2334 | 0.4073 | 10548 | 25896 | |
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| 1.8816 | 14.0 | 4186 | 2.4669 | 0.0002 | [0.003189792663476874, 0.00016826518593303046, 0.000321853878339234, 0.0036900369003690036] | 0.2394 | 0.4116 | 10658 | 25896 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.2 |
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