8a1e750             c31379b                                                                                                                                                           1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586 --- tags: - summarization widget: - text: '''with open ( CODE_STRING , CODE_STRING ) as in_file : buf = in_file . readlines ( ) with open ( CODE_STRING , CODE_STRING ) as out_file : for line in buf : if line == " ; Include this text " : line = line + " Include below " out_file . write ( line ) ''' --- # CodeTrans model for source code summarization python Pretrained model on programming language python using the t5 large model architecture. It was first released in [this repository](https://github.com/agemagician/CodeTrans). This model is trained on tokenized python code functions: it works best with tokenized python functions. ## Model description This CodeTrans model is based on the t5-large model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the source code summarization task for the python code snippets. ## Intended uses & limitations The model could be used to generate the description for the python function or be fine-tuned on other python code tasks. It can be used on unparsed and untokenized python code. However, if the python code is tokenized, the performance should be better. ### How to use Here is how to use this model to generate python function documentation using Transformers SummarizationPipeline: python from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline pipeline = SummarizationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_python_multitask_finetune"), tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_python_multitask_finetune", skip_special_tokens=True), device=0 ) tokenized_code = '''with open ( CODE_STRING , CODE_STRING ) as in_file : buf = in_file . readlines ( ) with open ( CODE_STRING , CODE_STRING ) as out_file : for line in buf : if line == " ; Include this text " : line = line + " Include below " out_file . write ( line ) ''' pipeline([tokenized_code])  Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/source%20code%20summarization/python/large_model.ipynb). ## Training data The supervised training tasks datasets can be downloaded on [Link](https://www.dropbox.com/sh/488bq2of10r4wvw/AACs5CGIQuwtsD7j_Ls_JAORa/finetuning_dataset?dl=0&subfolder_nav_tracking=1) ## Training procedure ### Multi-task Pretraining The model was trained on a single TPU Pod V3-8 for 500,000 steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. ### Fine-tuning This model was then fine-tuned on a single TPU Pod V2-8 for 100 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing python code. ## Evaluation results For the source code summarization tasks, different models achieves the following results on different programming languages (in BLEU score): Test results : | Language / Model | Python | SQL | C# | | -------------------- | :------------: | :------------: | :------------: | | CodeTrans-ST-Small | 8.45 | 17.55 | 19.74 | | CodeTrans-ST-Base | 9.12 | 15.00 | 18.65 | | CodeTrans-TF-Small | 10.06 | 17.71 | 20.40 | | CodeTrans-TF-Base | 10.94 | 17.66 | 21.12 | | CodeTrans-TF-Large | 12.41 | 18.40 | 21.43 | | CodeTrans-MT-Small | 13.11 | 19.15 | 22.39 | | CodeTrans-MT-Base | **13.37** | 19.24 | 23.20 | | CodeTrans-MT-Large | 13.24 | 19.40 | **23.57** | | CodeTrans-MT-TF-Small | 12.10 | 18.25 | 22.03 | | CodeTrans-MT-TF-Base | 10.64 | 16.91 | 21.40 | | CodeTrans-MT-TF-Large | 12.14 | **19.98** | 21.10 | | CODE-NN | -- | 18.40 | 20.50 | > Created by [Ahmed Elnaggar](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/) and Wei Ding | [LinkedIn](https://www.linkedin.com/in/wei-ding-92561270/)