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
  
  ## Table of Contents
  - [Model Details](#model-details)
  - [How to Get Started With the Model](#how-to-get-started-with-the-model)
  - [Uses](#uses)
  - [Risks, Limitations and Biases](#risks-limitations-and-biases)
  - [Training](#training)
  - [Evaluation](#evaluation)
  - [Environmental Impact](#environmental-impact)
  - [Citation Information](#citation-information)
  
  ## Model Details
  - **Model Description:** This CodeTrans model is based on the `t5-small` model. It has its own SentencePiece vocabulary model. It used transfer-learning pre-training on 7 unsupervised datasets in the software development domain. It is then fine-tuned on the program synthesis task for the lisp inspired DSL code.
  - **Developed by:** [Ahmed Elnaggar](https://www.linkedin.com/in/prof-ahmed-elnaggar/),[Wei Ding](https://www.linkedin.com/in/wei-ding-92561270/)
  - **Model Type:** Summarization
  - **Language(s):** English
  - **License:** Unknown
  - **Resources for more information:**
  	- [Research Paper](https://arxiv.org/pdf/2104.02443.pdf)
      - [GitHub Repo](https://github.com/agemagician/CodeTrans)
  
  
  ## How to Get Started With the Model
  
  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_small_program_synthese_transfer_learning_finetune"),
      tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_program_synthese_transfer_learning_finetune", 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/transfer%20learning%20fine-tuning/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)
  
  
  
  
  ## Uses
  
  #### Direct Use
  
  The model could be used to generate lisp inspired DSL code given the human language description tasks.
  
  ## Risks, Limitations and Biases
  
  
  As detailed in this model’s [publication](https://arxiv.org/pdf/2104.02443.pdf), this model makes use of the data-set [One Billion Word Language Model Benchmark corpus](https://www.researchgate.net/publication/259239818_One_Billion_Word_Benchmark_for_Measuring_Progress_in_Statistical_Language_Modeling) in order to gather the self-supervised English data samples. 
  
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
  As such, it should be noted that language models that are pretrained from text corpus such as the One Billion Word Word Language Model Benchmark corpus have been further explored (e.g by  [Ngo, Helen & Araújo et al(2021)](https://www.researchgate.net/publication/355582954_No_News_is_Good_News_A_Critique_of_the_One_Billion_Word_Benchmark) reports that the One Billion Word Word Language Model Benchmark corpus
  > “generate text in the linguistic style of news, without any grounding in the real world. In addition to potential harms from models which are inadvertently optimized for generating fake news.” 
  
  The aforementioned publication continues to warn that the One Billion Word Word Language Model Benchmark corpus
  > contains sentences which contain words commonly found on blocklists. While these sentences may have plausibly been used in expository contexts within the article, the destructive sentence-level preprocessing and shuffling applied to lm1b [One Billion Word Word Language Model Benchmark corpus] removes all long-range structure from the text and makes it infeasible to track the context and intent of individual examples. 
  
  [Ngo, Helen & Araújo et al(2021)](https://www.researchgate.net/publication/355582954_No_News_is_Good_News_A_Critique_of_the_One_Billion_Word_Benchmark)
  
  ## Training
  
  #### 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)
  
  The authors provide additionally notes about the vocabulary used, in the [associated paper](https://arxiv.org/pdf/2104.02443.pdf): 
  
  > We used the SentencePiece model (Kudo, 2018) to construct the vocabulary for this research, as well as to decode and encode the input/output.
  
  
  ## Training procedure
  
  #### Preprocessing
  
  ##### Transfer-learning Pretraining
  
  The mo