This repository contains the documentation for ActiveTigger, a collaborative text annotation web tool designed for computational social sciences. ActiveTigger assists researchers in exploring, annotating, and classifying text datasets using active learning and BERT model fine-tuning — without writing a single line of code.
The documentation is built with MkDocs and available at: https://activetigger.github.io/documentation/
- First steps — Quickstart guide, use cases, access, environmental impact
- Functionalities — Detailed pages for each feature (annotation, codebook, models, export, generative AI, etc.)
- Theoretical concepts — Key concepts and glossary
- Showcase — Real-world use cases
- Software — Architecture and contributors
- FAQ — Common questions
Make sure you have Python and MkDocs installed:
pip install mkdocsThen serve the documentation locally:
mkdocs serveThe site will be available at http://127.0.0.1:8000.
Contributions are welcome! Here's how you can help:
If you spot an error, a typo, or missing information, please open an issue describing the problem.
- Fork this repository
- Create a branch for your changes (
git checkout -b my-contribution) - Edit or add Markdown files in the
docs/directory - Preview your changes locally with
mkdocs serve - Submit a pull request with a clear description of what you changed and why
- Keep language clear and accessible — the audience is social science researchers, not developers
- Use admonitions (
!!! info,!!! warning) to highlight tips and important notes - Add screenshots or diagrams when they help illustrate a workflow
- If you add a new page, register it in
mkdocs.ymlunder the appropriate section
- Suggest new use cases or tutorials based on your experience with ActiveTigger
- Improve existing pages with clearer explanations or updated screenshots
- Translate documentation into other languages
- Join the Discord community to help answer questions from other users