Contributing

Thanks for considering contributing! We want PyTorch-NLP to be the way to do cutting-edge NLP research, but we cannot get there without community support.

How Can I Contribute?

Did you find a bug?

First, do a quick search to see whether your issue has already been reported. If your issue has already been reported, please comment on the existing issue.

Otherwise, open a new GitHub issue. Be sure to include a clear title and description. The description should include as much relevant information as possible. The description should explain how to reproduce the erroneous behavior as well as the behavior you expect to see. Ideally you would include a code sample or an executable test case demonstrating the expected behavior.

Did you write a fix for a bug?

Open a new GitHub pull request with the fix. Make sure you have a clear description of the problem and the solution, and include a link to relevant issues.

Once your pull request is created, our continuous build system will check your pull request. Continuous build will test that:

pytest All tests pass
flake8 accepts the code style (our guidelines are based on PEP8)
The docs can be generated successfully
Test coverage remains high. Please add unit tests so we maintain our code coverage.

If your code fails one of these checks, you will be expected to fix your pull request before it is considered.

You can run most of these tests locally with ./build_tools/travis/*, which will be faster than waiting for cloud systems to run tests.

Do you have a suggestion for an enhancement?

We use GitHub issues to track enhancement requests. Before you create an enhancement request:

Make sure you have a clear idea of the enhancement you would like. If you have a vague idea, consider discussing it first on the users list.
Check the documentation to make sure your feature does not already exist.
Do a quick search to see whether your enhancement has already been suggested.

When creating your enhancement request, please:

Provide a clear title and description.
Explain why the enhancement would be useful. It may be helpful to highlight the feature in other libraries.
Include code examples to demonstrate how the enhancement would be used.

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