We introduce the CreateDebate Arguments Dataset, a large-scale, unfiltered collection of 383,220 arguments from 7,435 of the most active for/against debates on the CreateDebate online debate platform, spanning 16 years of activity. Unlike existing computational argumentation resources, the dataset combines large scale with hierarchical debate-tree structure, author-assigned stance labels, community vote scores, and relational tags that explicitly mark when one argument disputes another. Because the full dataset is unfiltered, we release it under a gated access agreement and additionally release an openly available, manually curated subset of 84,872 arguments from 578 debates. To demonstrate the dataset’s utility, we define two counter-argument retrieval benchmarks on the curated subset: a targeted setting that uses the dispute tag as author-verified ground truth, and a general setting in which any opposing-side argument is a valid counter. Evaluating a BM25 baseline, a frozen bi-encoder, and a fine-tuned bi-encoder, we find consistent but bounded gains from fine-tuning on disputed pairs, a negative effect of debate-level context on within-debate retrieval, and limited cross-task transfer between the targeted and general settings. A qualitative analysis surfaces failure modes specific to naturally occurring debate. The dataset and models are released to the community.