Add Convence/ParseEmbed as an official benchmark on the Hub (If possible)

A user requests Hugging Face to add Convence/ParseEmbed as an official benchmark on the Hub. ParseEmbed is a retrieval benchmark for embedding models that tests parse-sensitive meaning under hard negatives. The dataset includes an eval.yaml configuration and is designed to evaluate semantic scope, formatting-sensitive text, and table grounding.

Hi Hugging Face team, I’d like to register Convence/ParseEmbed as an official benchmark on the Hub. Dataset: Convence/ParseEmbed · Datasets at Hugging Face https://huggingface.co/datasets/Convence/ParseEmbed It includes a root eval.yaml with: - name: ParseEmbed - evaluation framework: mteb - tasks: mean, text formatting, table - config: parse-embed ParseEmbed is a retrieval benchmark for embedding models. It tests whether models preserve parse-sensitive meaning under hard negatives, including semantic scope, formatting-sensitive text, and table grounding. The dataset card documents the purpose, files, task IDs, and usage. The dataset loads with datasets using the parse-embed config and the task splits. Could you please add it to the official benchmark allow-list? Thanks if something is wrong let me know By any chance if a hf staff is reading this, respond please if you have time