| --- |
| 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 small model architecture. It was first released in |
| [this repository](https://github.com/agemagician/CodeTrans). |
|
|
|
|
| ## Model description |
|
|
| This CodeTrans model is based on the `t5-small` 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. |
|
|
| ## 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_small_api_generation_multitask"), |
| tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_api_generation_multitask", 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/pre-training/api%20generation/small_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. |
|
|
|
|
| ## 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/) |
|
|
|
|
