readme: ---
  language: 
    - "code"
  thumbnail: "https://to-be-updated"
  tags:
    - code generation
    - code translation
    - bug fixing
  license: "mit"
  datasets:
    - CodeSearchNet
    - CodeXGLUE
  metrics:
    - EM
    - CodeBLEU
  ---
  
  Pretrained model for NatGen: Generative Pre-training by “Naturalizing” Source Code [[`paper`]](https://dl.acm.org/doi/abs/10.1145/3540250.3549162),[[`code`]](https://github.com/saikat107/NatGen),[[`slide`]](https://docs.google.com/presentation/d/1T6kjiohAAR1YvcNvTASR94HptA3xHGCl/edit?usp=sharing&ouid=111755026725574085503&rtpof=true&sd=true).
  
  To load the model,
  
  ```
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  tokenizer = AutoTokenizer.from_pretrained("saikatc/NatGen")
  model = AutoModelForSeq2SeqLM.from_pretrained("saikatc/NatGen")
  ```
  
  
  For citation,
  ```
  @inproceedings{chakraborty2022natgen,
      author = {Chakraborty, Saikat and Ahmed, Toufique and Ding, Yangruibo and Devanbu, Premkumar T. and Ray, Baishakhi},
      title = {NatGen: Generative Pre-Training by “Naturalizing” Source Code},
      year = {2022},
      isbn = {9781450394130},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3540250.3549162},
      doi = {10.1145/3540250.3549162},
      booktitle = {Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
      pages = {18–30},
      numpages = {13},
      keywords = {Neural Network, Semantic Preserving Transformation, Source Code Transformer, Source Code Pre-training},
      location = {Singapore, Singapore},
      series = {ESEC/FSE 2022}
  }
  ```
  
  