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