MultimodalHugs: Enabling Sign Language Processing in Hugging Face (arxiv.org)

Researchers introduced MultimodalHugs, a framework built on top of Hugging Face that adapts the popular NLP ecosystem for sign language processing (SLP) and other nonstandard multimodal tasks. Motivated by a survey showing SLP researchers struggle with fragmented, ad‑hoc code and low reproducibility, the project adds an abstraction layer that plugs into Hugging Face’s tooling while allowing richer data modalities and task types that don’t fit existing HF templates. The result is a platform that keeps Hugging Face’s advantages—model/dataset hubs, standardized training loops and evaluation—while enabling the SLP community to run fairer, more reproducible experiments. Technically, MultimodalHugs supports modality-specific inputs such as pose-estimation sequences and raw pixel data (e.g., for handshape/text character imagery), letting researchers treat these as first‑class citizens in HF pipelines. The paper includes quantitative experiments demonstrating the framework can accommodate diverse data representations and tasks, and the authors release code, data and demos to encourage adoption. Beyond sign languages, the abstraction makes the system applicable to other multimodal ML problems that fall outside standard HF templates, promising faster iteration, easier benchmarking and improved reproducibility across the field.