{"url":"/dataset/optmath-train","name":"OptMATH-Train","full_name":null,"description_markdown":"URL:https://huggingface.co/datasets/Aurora-Gem/OptMATH-Train","description_withheld":null,"homepage":"","introduced_date":"2025-02-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/optmath-a-scalable-bidirectional-data","title":"OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling","first_author":"Hongliang Lu","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["OptMATH-Train"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}