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CommitPredictorT5

Text2Text Generation
Transformers
PyTorch
t5
Generated from Trainer
text-generation-inference
Inference Endpoints
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update model card README.md

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@@ -3,7 +3,6 @@ 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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- - rouge
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  - bleu
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  model-index:
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  - name: CommitPredictorT5
@@ -17,13 +16,13 @@ should probably proofread and complete it, then remove this comment. -->
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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: 2.7383
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- - Rouge1: 0.0001
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- - Rouge2: 0.0
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- - Rougel: 0.0001
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- - Rougelsum: 0.0001
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- - Gen Len: 1.0
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- - Bleu: 0.0003
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  ## Model description
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@@ -43,36 +42,33 @@ More information needed
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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: 32
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- - eval_batch_size: 32
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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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  - num_epochs: 100
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu |
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- |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|
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- | 3.2223 | 1.0 | 837 | 2.6672 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0003 |
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- | 2.6296 | 2.0 | 1674 | 2.5416 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0003 |
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- | 2.4155 | 3.0 | 2511 | 2.4725 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0003 |
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- | 2.2666 | 4.0 | 3348 | 2.4331 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0003 |
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- | 2.112 | 5.0 | 4185 | 2.4343 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.9833 | 6.0 | 5022 | 2.4283 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.8833 | 7.0 | 5859 | 2.4360 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0003 |
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- | 1.7778 | 8.0 | 6696 | 2.4457 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.6767 | 9.0 | 7533 | 2.4696 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.5805 | 10.0 | 8370 | 2.4829 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.4918 | 11.0 | 9207 | 2.5202 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.4137 | 12.0 | 10044 | 2.5357 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0003 |
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- | 1.3351 | 13.0 | 10881 | 2.5621 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.2533 | 14.0 | 11718 | 2.5992 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0003 |
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- | 1.1952 | 15.0 | 12555 | 2.6149 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.122 | 16.0 | 13392 | 2.6565 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 1.0543 | 17.0 | 14229 | 2.6823 | 0.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0003 |
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- | 1.0017 | 18.0 | 15066 | 2.7106 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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- | 0.9437 | 19.0 | 15903 | 2.7383 | 0.0001 | 0.0 | 0.0001 | 0.0001 | 1.0 | 0.0003 |
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  ### Framework versions
 
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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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  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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  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