Package: commit
commit-autosuggestionsHave you ever hesitated to write a commit message? Now get a commit message from Artificial Intelligence!AbstractCodeBERT: A Pre-Trained Model for Programming and Natural Languagesintroduces a pre-trained model in a combination of Program Language and Natural Language(PL-NL). It also introduces the problem of converting code into natural language (Code Documentation Generation).diff --git a/test.py b/test.py
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+
+def add(a, b):
+    return a + bRecommended Commit Message : Add two arguments .We can use CodeBERT to create a model that generates a commit message when code is added. However, most code changes are not made only by add of the code, and some parts of the code are deleted.diff --git a/test.py b/test.py
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+import torch
+import arguments

 def add(a, b):
     return a + bRecommended Commit Message : Remove unused importsTo solve this problem, use a new embedding calledpatch_type_embeddingsthat can distinguish added and deleted, just as the sample et al, 2019 (XLM) used language embeddeding. (1 for added, 2 for deleted.)Language supportLanguageAddedDiffPython✅✅JavaScript⬜⬜Go⬜⬜JAVA⬜⬜Ruby⬜⬜PHP⬜⬜✅ — Supported🔶 — Partial support🚧 — Under development⬜ - N/A ️We plan to slowly conquer languages that are not currently supported. However, I also need to use expensive GPU instances of AWS or GCP to train about the above languages. Please do a simple sponsor for this!Quick StartTo run this project, you need a flask-based inference server (GPU) and a client (commit module). If you don't have a GPU, don't worry, you can use it through Google Colab.1. Run flask pytorch server.Prepare Docker and Nvidia-docker before running the server.1-a. If you have GPU machine.Serve flask server with Nvidia Docker$dockerrun-it--gpus0-p5000:5000commit-autosuggestions:0.1-gpu1-b. If you don't have GPU machine.Even if you don't have a GPU, you can still serve the flask server by using the ngrok setting incommit_autosuggestions.ipynb.2. Start commit autosuggestion with Python client module namedcommit.First, install the package through pip.$pipinstallcommitSet the endpoint for the flask server configured in step 1 through the commit configure command. (For example, if the endpoint ishttp://127.0.0.1:5000, set it as follows:commit configure --endpoint http://127.0.0.1:5000)$commitconfigure--helpUsage:commitconfigure[OPTIONS]Options:--profileTEXTuniquenameformanagingeachindependentsettings--endpointTEXTendpointaddressaccessibletotheserver(example:http://127.0.0.1:5000/)[required]--helpShowthismessageandexit.All setup is done! Now, you can get a commit message from the AI with the command commit.$commit--helpUsage:commit[OPTIONS]COMMAND[ARGS]...

Options:--profileTEXTuniquenameformanagingeachindependentsettings-f,--fileFILENAMEpatchfilecontaininggitdiff(e.g.filecreatedby`gitadd`and`gitdiff--cached>test.diff`)-v,--verboseprintsuggestedcommitmessagemoredetail.-a,--autocommitautomaticallycommitwithoutaskingifyouwanttocommit--helpShowthismessageandexit.

Commands:configureTraining detailReferHow to train for your lint style. This allows you to re-fine tuning to your repository's commit lint style.ContributionYou can contribute anything, even a typo or code in the article. Don't hesitate!!.
Versions are managed only within the branch with the name of each version. After being released on Pypi, it is merged into the master branch and new development proceeds in the upgraded version branch.AuthorTae Hwan Jung(@graykode)