{"url":"/dataset/p2","name":"P2","full_name":"PartiPrompts","description_markdown":"**PartiPrompts (P2)** is a rich set of over **1600 prompts in English** that we release as part of this work. P2 can be used to measure model capabilities across various categories and challenge aspects. These prompts can be simple, allowing us to gauge the progress from scaling¹². If you're curious to explore more, you can find the dataset on **Hugging Face**¹ or visit the official **Parti website**².\r\n\r\nSource: Conversation with Bing, 3/18/2024\r\n(1) nateraw/parti-prompts · Datasets at Hugging Face. https://huggingface.co/datasets/nateraw/parti-prompts.\r\n(2) Parti: Pathways Autoregressive Text-to-Image Model. https://parti.research.google/.\r\n(3) GitHub - google-research/parti. https://github.com/google-research/parti.","description_withheld":null,"homepage":"https://huggingface.co/datasets/nateraw/parti-prompts","introduced_date":"2022-06-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/scaling-autoregressive-models-for-content","title":"Scaling Autoregressive Models for Content-Rich Text-to-Image Generation","first_author":"Jiahui Yu","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["P2"],"data_loaders":[],"num_papers_in_archive":54,"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."}