{"url":"/dataset/bimed1-3m","name":"BiMed1.3M","full_name":null,"description_markdown":"The dataset covers three types of medical interactions in both English and Arabic:   \r\n- Multiple-choice question answering (MCQA), focusing on specialized medical knowledge.     \r\n- Open question answering (QA), including real-world consumer questions.  \r\n- MCQA-Grounded multi-turn chat conversations for dynamic exchanges.  \r\n\r\nA semi-automated translation pipeline with human alignment was used to create high-quality Arabic versions.\r\nThe BiMed1.3M dataset results from translating 444,995 English samples into Arabic and mixing Arabic and English in a 1:2 ratio.","description_withheld":null,"homepage":"","introduced_date":"2024-02-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/bimedix-bilingual-medical-mixture-of-experts","title":"BiMediX: Bilingual Medical Mixture of Experts LLM","first_author":"Sara Pieri","url":null},"license":null,"modalities":[],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Arabic","url":"/datasets/language/arabic"}],"variants":["BiMed1.3M"],"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."}