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.
- Stave: Provide visualization and annotation for NLP tasks, by providing generic UI elements based on the abstraction.
- 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.processors: Processors are core components that perform NLP tasks. We also implements several 3-rd party wrappers here.
- forte.trainer: Contains components that handles training.
- forte.models: Contains our in-house development of some NLP models.
- forte.common: configuration, exceptions and other sharable code.
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:
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 follows the Google Python Style guide. The project code is examined using pylint and flake8, which will be run automatically in Travis 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.