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---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- code_search_net
metrics:
- bleu
model-index:
- name: base_model_base_tokenizer
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: code_search_net
type: code_search_net
config: python
split: test
args: python
metrics:
- name: Bleu
type: bleu
value: 0.07436414625113424
---
<!-- 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. -->
# base_model_base_tokenizer
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the code_search_net dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1017
- Bleu: 0.0744
- Precisions: [0.37389569483256924, 0.14063645643779682, 0.07580332788787783, 0.045527148854836816]
- Brevity Penalty: 0.6407
- Length Ratio: 0.6920
- Translation Length: 585436
- Reference Length: 846059
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Bleu | Brevity Penalty | Length Ratio | Validation Loss | Precisions | Reference Length | Translation Length |
|:-------------:|:-----:|:------:|:------:|:---------------:|:------------:|:---------------:|:-------------------------------------------------------------------------------------:|:----------------:|:------------------:|
| 2.4273 | 1.0 | 25762 | 0.0665 | 0.6794 | 0.7212 | 2.3438 | [0.34926724858481134, 0.12159425046725157, 0.062078959459937084, 0.03489467043820187] | 846059 | 610166 |
| 2.3512 | 2.0 | 51524 | 0.0733 | 0.7181 | 0.7512 | 2.2643 | [0.3534451290507329, 0.1262343107830303, 0.06531254968421979, 0.03721425521409004] | 846059 | 635564 |
| 2.2525 | 3.0 | 77286 | 0.0691 | 0.6453 | 0.6954 | 2.2234 | [0.36523755211936504, 0.1318932094567742, 0.06891201805888993, 0.03961906221856018] | 846059 | 588313 |
| 2.2252 | 4.0 | 103048 | 0.0726 | 0.7043 | 0.7404 | 2.1949 | [0.3601686933924165, 0.1283373434960897, 0.06578382296859486, 0.0371541685491374] | 846059 | 626462 |
| 2.1523 | 5.0 | 128810 | 0.0703 | 0.6506 | 0.6994 | 2.1769 | [0.3663069159346027, 0.1334874876878427, 0.06959109409366254, 0.040003198275976946] | 846059 | 591706 |
| 2.1027 | 6.0 | 154572 | 0.0650 | 0.5879 | 0.6531 | 2.1585 | [0.37335963586676196, 0.13614151644150174, 0.07119404952304512, 0.04138235959446398] | 846059 | 552545 |
| 2.0458 | 7.0 | 180334 | 0.0682 | 0.6176 | 0.6748 | 2.1491 | [0.37062538973004405, 0.1355146147678402, 0.07123664846902444, 0.04155352506292986] | 846059 | 570908 |
| 2.0594 | 8.0 | 206096 | 0.0702 | 0.6407 | 0.6919 | 2.1403 | [0.3700899171204657, 0.13524405355792343, 0.07062960711230036, 0.04081911815137772] | 846059 | 585428 |
| 2.0459 | 9.0 | 231858 | 0.0635 | 0.5682 | 0.6388 | 2.1327 | [0.37916909499625345, 0.13810659289354987, 0.07176079868122479, 0.04160453545539102] | 846059 | 540495 |
| 2.0029 | 10.0 | 257620 | 0.0684 | 0.6128 | 0.6713 | 2.1264 | [0.3745439691237164, 0.13731087325347474, 0.07204645620574554, 0.04194087964799725] | 846059 | 567944 |
| 2.0107 | 11.0 | 283382 | 0.0697 | 0.6139 | 0.6721 | 2.1202 | [0.37538600600727345, 0.13908031254002817, 0.07356968494927149, 0.04326375560457764] | 846059 | 568644 |
| 1.995 | 12.0 | 309144 | 0.0790 | 0.7220 | 0.7543 | 2.1192 | [0.3595232536092102, 0.1336969667453998, 0.07124298456393582, 0.04192048242921579] | 846059 | 638159 |
| 1.9653 | 13.0 | 334906 | 0.0750 | 0.6727 | 0.7161 | 2.1158 | [0.3663186076760047, 0.13635359040297698, 0.07246562633002641, 0.04279559846361466] | 846059 | 605836 |
| 1.9811 | 14.0 | 360668 | 0.0718 | 0.6325 | 0.6858 | 2.1096 | [0.37342310979981247, 0.13867710694415825, 0.0736328303569596, 0.043440268414579084] | 846059 | 580256 |
| 1.9745 | 15.0 | 386430 | 0.0741 | 0.6592 | 0.7059 | 2.1060 | [0.36869699176985743, 0.13724429728380805, 0.07301699268383118, 0.04318353520566863] | 846059 | 597195 |
| 1.939 | 16.0 | 412192 | 0.0706 | 0.6166 | 0.6740 | 2.1063 | [0.37537898781101553, 0.13979047848408885, 0.0742785001701673, 0.04399835661136439] | 846059 | 570269 |
| 1.9177 | 17.0 | 437954 | 0.0757 | 0.6671 | 0.7118 | 2.1063 | [0.37017425883954735, 0.13833476986726426, 0.07389756751525232, 0.04386076232849102] | 846059 | 602265 |
| 1.9265 | 18.0 | 463716 | 0.0717 | 0.6192 | 0.6760 | 2.1016 | [0.37650650333865443, 0.14089062050951845, 0.075366455530664, 0.045028150012067114] | 846059 | 571937 |
| 1.9622 | 19.0 | 489478 | 0.0730 | 0.6288 | 0.6831 | 2.1022 | [0.3746837721013452, 0.1407333566053557, 0.07570910522025132, 0.045477562304123496] | 846059 | 577906 |
| 1.9171 | 20.0 | 515240 | 2.1017 | 0.0744 | [0.37389569483256924, 0.14063645643779682, 0.07580332788787783, 0.045527148854836816]| 0.6407 | 0.6920 | 585436 | 846059 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
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