  Recent advancements in Large Language Models (LLMs) have led to high-quality
Machine-Generated Text (MGT), giving rise to countless new use cases and
applications. However, easy access to LLMs is posing new challenges due to
misuse. To address malicious usage, researchers have released datasets to
effectively train models on MGT-related tasks. Similar strategies are used to
compile these datasets, but no tool currently unifies them. In this scenario,
we introduce TextMachina, a modular and extensible Python framework, designed
to aid in the creation of high-quality, unbiased datasets to build robust
models for MGT-related tasks such as detection, attribution, mixcase, or
boundary detection. It provides a user-friendly pipeline that abstracts away
the inherent intricacies of building MGT datasets, such as LLM integrations,
prompt templating, and bias mitigation. The quality of the datasets generated
by TextMachina has been assessed in previous works, including shared tasks
where more than one hundred teams trained robust MGT detectors.
