--- license: apache-2.0 base_model: t5-base tags: - generated_from_trainer datasets: - code_search_net metrics: - bleu model-index: - name: base_model_custom_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: 0.0418696919911329 --- # base_model_custom_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: 2.9297 - Bleu: 0.0419 - Precisions: [0.16646886171883812, 0.051341379400381214, 0.025538496667355304, 0.01408001744219341] - Brevity Penalty: 1.0 - Length Ratio: 1.9160 - Translation Length: 1515803 - Reference Length: 791127 ## 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 | |:-------------:|:-----:|:------:|:------:|:---------------:|:------------:|:---------------:|:---------------------------------------------------------------------------------------:|:----------------:|:------------------:| | 3.9604 | 1.0 | 25762 | 0.0311 | 1.0 | 2.0901 | 3.8577 | [0.12981129473835085, 0.037916946342151155, 0.018860549385742668, 0.010123458812721054] | 791127 | 1653531 | | 3.7556 | 2.0 | 51524 | 0.0304 | 1.0 | 2.0887 | 3.5650 | [0.12978779415458075, 0.037579383019195466, 0.018120049525730805, 0.00967159578808246] | 791127 | 1652405 | | 3.5524 | 3.0 | 77286 | 0.0337 | 1.0 | 2.0745 | 3.4150 | [0.1400710094937268, 0.04118126290523918, 0.0203289377688518, 0.01095848654003696] | 791127 | 1641189 | | 3.4698 | 4.0 | 103048 | 0.0340 | 1.0 | 2.0788 | 3.3056 | [0.14277601173291565, 0.041700438046903744, 0.020391137906857287, 0.010998711103394348] | 791127 | 1644604 | | 3.3163 | 5.0 | 128810 | 0.0377 | 1.0 | 2.0193 | 3.2312 | [0.15481298837386176, 0.04617083876865068, 0.022825576079888228, 0.012408874977873952] | 791127 | 1597521 | | 3.2458 | 6.0 | 154572 | 0.0382 | 1.0 | 1.9276 | 3.1719 | [0.1593547435203856, 0.04704355006890476, 0.023023369844916947, 0.012389103841794662] | 791127 | 1524975 | | 3.1574 | 7.0 | 180334 | 0.0373 | 1.0 | 2.0231 | 3.1267 | [0.15301209486452477, 0.04557636504175273, 0.022512350851579006, 0.012331176442211789] | 791127 | 1600514 | | 3.1398 | 8.0 | 206096 | 0.0386 | 1.0 | 1.9724 | 3.0893 | [0.1577822509066417, 0.04745355472604797, 0.023342833604973825, 0.012766267921605798] | 791127 | 1560429 | | 3.0691 | 9.0 | 231858 | 0.0399 | 1.0 | 1.9159 | 3.0574 | [0.16179891666501725, 0.0490436396529825, 0.024170720153435545, 0.013205125551162357] | 791127 | 1515690 | | 3.0536 | 10.0 | 257620 | 0.0410 | 1.0 | 1.8550 | 3.0321 | [0.1656489584760067, 0.05027218283158705, 0.024914277684092188, 0.013668271409759075] | 791127 | 1467513 | | 3.0379 | 11.0 | 283382 | 0.0404 | 1.0 | 1.8928 | 3.0082 | [0.1630008107267023, 0.049590989569352824, 0.02452930558336929, 0.013463575807213558] | 791127 | 1497422 | | 3.0183 | 12.0 | 309144 | 0.0409 | 1.0 | 1.9428 | 2.9924 | [0.16253787482001938, 0.049984123536708294, 0.02498794115282579, 0.01380309274144192] | 791127 | 1536971 | | 2.9442 | 13.0 | 334906 | 0.0413 | 1.0 | 1.9288 | 2.9773 | [0.16426924674922966, 0.05052962811986506, 0.025225357778251727, 0.013893123599262487] | 791127 | 1525946 | | 2.9746 | 14.0 | 360668 | 0.0411 | 1.0 | 1.9154 | 2.9622 | [0.16395222297528722, 0.050373776569881686, 0.02506334156586741, 0.013817874614866431] | 791127 | 1515289 | | 2.9556 | 15.0 | 386430 | 0.0416 | 1.0 | 1.8903 | 2.9505 | [0.16631916674913938, 0.05114349827528396, 0.025291167834370104, 0.013919582587470626] | 791127 | 1495444 | | 2.9423 | 16.0 | 412192 | 0.0415 | 1.0 | 1.9161 | 2.9441 | [0.1656048056193977, 0.050903942131636466, 0.02527336097239107, 0.013901882376966617] | 791127 | 1515892 | | 2.9257 | 17.0 | 437954 | 0.0417 | 1.0 | 1.9204 | 2.9387 | [0.16566872310834463, 0.051149695919205686, 0.02547749541013215, 0.01403388257902964] | 791127 | 1519291 | | 2.9023 | 18.0 | 463716 | 0.0417 | 1.0 | 1.9252 | 2.9331 | [0.16569868978430946, 0.05118214894137258, 0.025432645752525008, 0.014019028423183673] | 791127 | 1523108 | | 2.946 | 19.0 | 489478 | 0.0420 | 1.0 | 1.9138 | 2.9301 | [0.16682044755191178, 0.051534782710695386, 0.02563003483561942, 0.014141190855303378] | 791127 | 1514059 | | 2.8761 | 20.0 | 515240 | 2.9297 | 0.0419 | [0.16646886171883812, 0.051341379400381214, 0.025538496667355304, 0.01408001744219341]| 1.0 | 1.9160 | 1515803 | 791127 | ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2