readme: ---
  tags:
  - summarization
  widget:
  - text: "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
  
  ---
  
  
  # CodeTrans model for program synthesis
  Pretrained model on programming language lisp inspired DSL 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.
  
  ## Intended uses & limitations
  
  The model could be used to generate lisp inspired DSL code given the human language description tasks.
  
  ### How to use
  
  Here is how to use this model to generate lisp inspired DSL code using Transformers SummarizationPipeline:
  
  ```python
  from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline
  
  pipeline = SummarizationPipeline(
      model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_program_synthese_multitask"),
      tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_program_synthese_multitask", skip_special_tokens=True),
      device=0
  )
  
  tokenized_code = "you are given an array of numbers a and a number b , compute the difference of elements in a and b"
  pipeline([tokenized_code])
  ```
  Run this example in [colab notebook](https://github.com/agemagician/CodeTrans/blob/main/prediction/multitask/pre-training/program%20synthesis/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 360,000 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   |      LISP      |
  | -------------------- | :------------: |
  |   CodeTrans-ST-Small    |     89.43      |
  |   CodeTrans-ST-Base     |     89.65      |
  |   CodeTrans-TF-Small    |     90.30      |
  |   CodeTrans-TF-Base     |     90.24      |
  |   CodeTrans-TF-Large    |     90.21      |
  |   CodeTrans-MT-Small    |     82.88      |
  |   CodeTrans-MT-Base     |     86.99      |
  |   CodeTrans-MT-Large    |     90.27      |
  |   CodeTrans-MT-TF-Small |   **90.31**    |
  |   CodeTrans-MT-TF-Base  |     90.30      |
  |   CodeTrans-MT-TF-Large |     90.17      |
  |   State of the art   |     85.80      |
  
  
  
  > 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/)
  
  