Contribution Guideline

GluonNLP community welcomes contributions from anyone! Latest documentation can be found here.

There are lots of opportunities for you to become our contributors:

For a list of open starter tasks, check good first issues.

Clone the master branch for development

GluonNLP github defaults to the latest stable branch. For development, please make changes to the master branch such that the changes will be included in the next release.

git clone -b master

Make changes

Our package uses continuous integration and code coverage tools for verifying pull requests. Before submitting, contributor should ensure that the following checks do not fail:

  • Lint (code style)

  • Unittest

  • Doctest

The commands executed by the continuous integration server to perform the tests are listed in the build_steps.groovy file.

Contribute to model zoo

The model zoo in GluonNLP provide training scripts for reproducing state-of-the-art (SOTA) results and for applying them in specific application. The scripts are intended for practitioners who are familiar with the libraries to tweak and hack. When contributing scripts, we request that you provide training logs. You can upload the logs here through pull requests, and then link them in the accompanying documentation for the scripts.

See existing examples.

Contribute tutorials

Our tutorials are intended for people who are interested in NLP and want to get better familiarized on different parts in NLP. In order for people to easily understand the content, the code needs to be clean and readable, accompanied by explanation with good writing.

See existing tutorials.

To make the review process easy, we adopt notedown as the tutorial format. Notedown notebooks are regular markdown files with code blocks that can be converted into Jupyter notebooks.

We suggest you start the example with Jupyter notebook. When the content is ready, please:

  • Clear the output cells in the jupyter notebook,

  • Install notedown.

  • Run notedown input.ipynb –to markdown >

  • Submit the .md file for review.

Notebook Guidelines:

  • Less is better. Only show the code that needs people’s attention.

  • Have a block upfront about the key takeaway of the notebook.

  • Explain the motivation of the notebook to guide readers. Add figures if they help.

  • Try to have < 10 lines of code per block, < 100 lines of code per notebook.

  • Hide uninteresting complex functions in .py and import them.

  • Hide uninteresting model parameters. We can make some of them default parameters in model definition. Maybe out of 30 we just show 5 interesting ones and pass those to model constructor.

  • Only import module instead of classes and functions (i.e. from gluonnlp import model and use model.get_model, instead of from gluonnlp.model import get_model)

  • Make tutorials more engaging, interactive, prepare practice questions for people to try it out. For example, for embedding evaluation, we can ask questions to the audience like what’s the most similar word to xxx.

  • Make sure the notebook can be zoomed in and still render well. This helps accommodate different viewing devices.

  • For low level APIs such as BeamSearch and Scorer, explain the API with examples so ppl know how to play with it / hack it.

Contribute new API

There are several different types of APIs, such as model definition APIs, public dataset APIs, and building block APIs.

Model definition APIs facilitate the sharing of pre-trained models. If you’d like to contribute models with pre-trained weights, you can open an issue and ping committers first, we will help with things such as hosting the model weights while you propose the patch.

Public dataset APIs facilitate the sharing of public datasets. Like model definition APIs, if you’d like to contribute new public datasets, you can open an issue and ping committers and review the dataset needs. If you’re unsure, feel free to open an issue anyway.

Finally, our data and model building block APIs come from repeated patterns in examples. It has the highest quality bar and should always starts from a good design. If you have an idea on proposing a new API, we encourage you to draft a design proposal first, so that the community can help iterate. Once the design is finalized, everyone who are interested in making it happen can help by submitting patches. For designs that require larger scopes, we can help set up GitHub project to make it easier for others to join.

Contribute Docs

Documentation is at least as important as code. Good documentation delivers the correct message clearly and concisely. If you see any issue in the existing documentation, a patch to fix is most welcome! To locate the code responsible for the doc, you may use “Edit on Github” in the top right corner, or the “[source]” links after each API. Also, git grep works nicely for searching for a specific string.