Thanks for considering contribute to Forte, a project of the ASYML family.

This file outlines the guidelines for contributing to Forte and ASYML projects. While the guideline cannot cover all scenarios, we ask everyone to be reasonable and make your bets judgments, and feel free to propose changes to this document via a pull request.

This project and everyone participating in it is governed by the Contributor Covenant Code of Conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to [email protected].

There are many ways you can contribute to ASYML projects. The goal of our projects is to modularize Machine Learning and NLP questions, and to make NLP/ML problems as standard engineering problems. Each project solves slightly different problems. Pick the problem you are most interested and get started!

• Texar(Texar-Pytorch): Modularize a complex ML model into smaller components at different levels.
• Forte: Decompose and abstract complex NLP problems into multiple modules, and standardize the interface between the sub-problems and ML interface.
• Forte Wrappers: Decompose and abstract complex NLP problems into multiple modules, and standardize the interface between the sub-problems and ML interface.
• Stave: Provide visualization and annotation for NLP tasks, by providing generic UI elements based on the abstraction.

We currently adopt a non-standard namespace packaging strategy. While this may introduce some constraints in development, this allows us to provide a unified user experience. The strategy, simply put, installs all Forte packages under "forte" module, but currently stored in two different repositories, as different projects.

• forte: The root package contains the pipeline implementations, and defines the pipeline_component.
• forte.data: contains main data relevant components, mainly implements the data pack system and the ontology system.
• forte.dataset: contains readers to popular datasets.
• forte.processors: Processors are core components that perform NLP tasks. This module currently contains some base processor implementations and simple processor examples. We have
• forte.models: Contains our in-house development of some NLP models.
• forte.common: configuration, exceptions and other sharable code.

• forte.xxx: Forte Wrapper contains adapters of third party tools. Each tool is placed in its own folder to avoid dependency conflicts. Each directory contains a standalone project and can be installed independently. The project will be installed as forte.xxx and under forte/xxx folder in the site-packages. For example, forte.nltk will be installed under site_packages/forte/nltk folder, and the tool can be imported via import forte.nltk and uninstalled via pip uninstall forte.nltk.

Bugs are tracked as GitHub issues. Search the issues to make sure the problem is not reported before.

To report a bug, create an issue provide the following information by filling in the bug report template.

In the bug template, make sure you include enough information for reproducing the problem:

• Use a descriptive title to identify the problem.
• Describe the steps to reproduce the problem, ideally a minimum code/command that can reproduce the problem.
• Describe the environment as detail as possible.
• Describe the actual behavior, and the expected behavior.

Enhancements are also tracked as issues. Similarly, Search the issues to make sure the enhancement is not suggested before. To suggest the enhancement, create an issue by filling in the feature enhancement template.

Following the feature template, fill in the information in more details:

• A clear and concise description of what the problem is.
• Describe the solution you'd like, with a clear and concise description of what you want to happen.
• Describe alternatives you've considered.
• Include as much context as possible.

When you have fixed a bug or implemented a new feature, you can create a pull request for review. Use the following simple PR templates to structure the PR:

• PR Template

We use standard issue labels such as priority, bug, enhancement, etc. We have a few topic labels to identify the type of the issue. Currently the topics are data ( problems in our data system), docs (problems about documentation), examples ( problems about the examples), interface (the interfaces between different modules) , model (machine learning models), ontology (the ontology system). We may have more topic labels in the future.

The programming language for Forte is Python. We follow the Google Python Style guide. The project code is examined using pylint, flake8, mypy and black, which will be run automatically in CI.

• Limit the first line to 72 characters or less
• Reference issues and pull requests in the second line
• For documentation only changes, use [skip ci] or [ci skip] in your commit messages to skip travis build.