---
license: bsd-3-clause
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: CommitPredictorT5
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# CommitPredictorT5

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.
It achieves the following results on the evaluation set:
- Loss: 2.4669
- Bleu: 0.0002
- Precisions: [0.003189792663476874, 0.00016826518593303046, 0.000321853878339234, 0.0036900369003690036]
- Brevity Penalty: 0.2394
- Length Ratio: 0.4116
- Translation Length: 10658
- Reference Length: 25896

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 42
- eval_batch_size: 42
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 126
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss | Bleu   | Precisions                                                                                   | Brevity Penalty | Length Ratio | Translation Length | Reference Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|
| No log        | 1.0   | 299  | 2.8109          | 0.0002 | [0.003640040444893832, 0.00019327406262079628, 0.0003745318352059925, 0.006024096385542169]  | 0.1982          | 0.3819       | 9889               | 25896            |
| 3.1102        | 2.0   | 598  | 2.6662          | 0.0002 | [0.004371150407311742, 0.00018691588785046728, 0.00036114120621162876, 0.005319148936170213] | 0.2074          | 0.3887       | 10065              | 25896            |
| 3.1102        | 3.0   | 897  | 2.5869          | 0.0002 | [0.0033418517790446234, 0.00018321729571271528, 0.0003546099290780142, 0.005494505494505495] | 0.2132          | 0.3928       | 10173              | 25896            |
| 2.6696        | 4.0   | 1196 | 2.5371          | 0.0002 | [0.0033398821218074658, 0.00018301610541727673, 0.0003522367030644593, 0.004672897196261682] | 0.2135          | 0.3931       | 10179              | 25896            |
| 2.6696        | 5.0   | 1495 | 2.5077          | 0.0002 | [0.003243655790879603, 0.0001734304543877905, 0.0003356831151393085, 0.005208333333333333]   | 0.2298          | 0.4047       | 10481              | 25896            |
| 2.4738        | 6.0   | 1794 | 2.4810          | 0.0002 | [0.0029016345874842827, 0.00017784101013693757, 0.00034234851078397807, 0.0045662100456621]  | 0.2220          | 0.3992       | 10338              | 25896            |
| 2.3139        | 7.0   | 2093 | 2.4625          | 0.0002 | [0.002756130013305455, 0.0001722356183258698, 0.00033101621979476995, 0.00423728813559322]   | 0.2319          | 0.4063       | 10521              | 25896            |
| 2.3139        | 8.0   | 2392 | 2.4556          | 0.0002 | [0.0027348170501697473, 0.00016983695652173913, 0.0003266906239790918, 0.004273504273504274] | 0.2364          | 0.4094       | 10603              | 25896            |
| 2.1842        | 9.0   | 2691 | 2.4470          | 0.0002 | [0.003198193961057285, 0.000169061707523246, 0.00032658393207054214, 0.004784688995215311]   | 0.2378          | 0.4105       | 10630              | 25896            |
| 2.1842        | 10.0  | 2990 | 2.4439          | 0.0002 | [0.0033203680865193054, 0.00017167381974248928, 0.000328515111695138, 0.0038022813688212928] | 0.2330          | 0.4070       | 10540              | 25896            |
| 2.0831        | 11.0  | 3289 | 2.4435          | 0.0002 | [0.0032796101949025486, 0.000167897918065816, 0.000321853878339234, 0.003875968992248062]    | 0.2401          | 0.4121       | 10671              | 25896            |
| 1.9685        | 12.0  | 3588 | 2.4483          | 0.0002 | [0.0037652056381540836, 0.0001772421127259837, 0.0003397893306150187, 0.004098360655737705]  | 0.2231          | 0.3999       | 10357              | 25896            |
| 1.9685        | 13.0  | 3887 | 2.4557          | 0.0002 | [0.0033178500331785005, 0.00017143836790673754, 0.000327653997378768, 0.0036900369003690036] | 0.2334          | 0.4073       | 10548              | 25896            |
| 1.8816        | 14.0  | 4186 | 2.4669          | 0.0002 | [0.003189792663476874, 0.00016826518593303046, 0.000321853878339234, 0.0036900369003690036]  | 0.2394          | 0.4116       | 10658              | 25896            |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.0+cu117
- Datasets 2.7.1
- Tokenizers 0.13.2