textsbut

textsbut is a framework and collection of tools designed for auditing and evaluating text data and language models. It provides standardized methods to assess data provenance, content quality, and the alignment between datasets and their documentation. The project emphasizes reproducibility, transparency, and modularity in NLP experimentation.

The name textsbut combines 'texts' with a contrastive conjunction 'but', signaling its focus on examining exceptions, contradictions, and quality gaps between textual content and descriptive metadata. It is an open, community-oriented project rather than the product of a single organization.

The concept emerged from concerns about data integrity in NLP workflows. An early prototype appeared in 2022 as an experimental toolkit developed by researchers at several universities, with industry partners contributing evaluation scripts. A stable 1.0 release followed in 2024, accompanied by expanded documentation and community-contributed evaluators.

Key components include a data catalog that records dataset provenance, licensing, languages, and annotations; an evaluation engine with scores for quality, bias, toxicity, and consistency; a model telemetry module that tracks generation metrics; and reproducibility tools for experiment tracking. The platform supports Python interfaces and command-line workflows, and it is designed to be language-agnostic through pluggable evaluators.

Applications cover dataset auditing, model evaluation, and content moderation workflows. Users can compare datasets against documentation, reproduce experiments, and benchmark model outputs under defined criteria. The project aims to facilitate transparent reporting in NLP research and production pipelines.

Reception has been mixed: scholars praise the emphasis on provenance and reproducibility, while critics point to complexity and the risk of over-standardization. Adoption often depends on community governance and the availability of high-quality evaluators. Ongoing work focuses on governance, interoperability, and expanding language coverage. Related topics include text mining, natural language processing, dataset bias, and evaluation metrics.