--- tags: - summarization widget: - text: "parse the uses licence node of this package , if any , and returns the license definition if theres" --- # CodeTrans model for api recommendation generation Pretrained model for api recommendation generation using the t5 base model architecture. It was first released in [this repository](https://github.com/agemagician/CodeTrans). ## Model description This CodeTrans model is based on the `t5-base` 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 api recommendation generation task for the java apis. ## Intended uses & limitations The model could be used to generate api usage for the java programming tasks. ### How to use Here is how to use this model to generate java function documentation using Transformers SummarizationPipeline: ```python from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline pipeline = SummarizationPipeline( model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_api_generation_multitask_finetune"), tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation_multitask_finetune", skip_special_tokens=True), device=0 ) tokenized_code = "parse the uses licence node of this package , if any , and returns the license definition if theres" pipeline([tokenized_code]) ``` Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/fine-tuning/api%20generation/base_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 320,000 steps in total, using sequence length 512 (batch size 256), using only the dataset only containing api recommendation generation data. ## Evaluation results For the code documentation tasks, different models achieves the following results on different programming languages (in BLEU score): Test results : | Language / Model | Java | | -------------------- | :------------: | | CodeTrans-ST-Small | 68.71 | | CodeTrans-ST-Base | 70.45 | | CodeTrans-TF-Small | 68.90 | | CodeTrans-TF-Base | 72.11 | | CodeTrans-TF-Large | 73.26 | | CodeTrans-MT-Small | 58.43 | | CodeTrans-MT-Base | 67.97 | | CodeTrans-MT-Large | 72.29 | | CodeTrans-MT-TF-Small | 69.29 | | CodeTrans-MT-TF-Base | 72.89 | | CodeTrans-MT-TF-Large | **73.39** | | State of the art | 54.42 | > 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/)