Model Hub
Browse PQC-verified AI models, datasets, and tools
Dataset Card for MONET MONET (Massive, Open, Non-redundant and Enriched Text-to-image dataset) is a large-scale, curated image-text dataset designed for training text-to-image (T2I) systems. It contains 104.9 million high-quality image-text pairs distilled from 2.9 billion raw pairs across nine heterogeneous open sources (6 real and 3 synthetic) through successive stages of safety filtering, domain-based filtering, exact and near-duplicate removal, and re-captioning with… See the full description on the dataset page: https://huggingface.co/datasets/jasperai/monet.
🍃 MINT-1T:Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens 🍃 MINT-1T is an open-source Multimodal INTerleaved dataset with 1 trillion text tokens and 3.4 billion images, a 10x scale-up from existing open-source datasets. Additionally, we include previously untapped sources such as PDFs and ArXiv papers. 🍃 MINT-1T is designed to facilitate research in multimodal pretraining. 🍃 MINT-1T is created by a team from the University of Washington in… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations/MINT-1T-PDF-CC-2023-06.
Dataset Card for Mostly Basic Python Problems (mbpp) Dataset Summary The benchmark consists of around 1,000 crowd-sourced Python programming problems, designed to be solvable by entry level programmers, covering programming fundamentals, standard library functionality, and so on. Each problem consists of a task description, code solution and 3 automated test cases. As described in the paper, a subset of the data has been hand-verified by us. Released here as part of… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/mbpp.
Dataset Card for "winogrande" Dataset Summary WinoGrande is a new collection of 44k problems, inspired by Winograd Schema Challenge (Levesque, Davis, and Morgenstern 2011), but adjusted to improve the scale and robustness against the dataset-specific bias. Formulated as a fill-in-a-blank task with binary options, the goal is to choose the right option for a given sentence which requires commonsense reasoning. Supported Tasks and Leaderboards More Information… See the full description on the dataset page: https://huggingface.co/datasets/allenai/winogrande.
🥞 The Stack v3 What is it? What is being released How to download and use it Dataset statistics Dataset structure Dataset creation Considerations for using the data Additional information What is it? The Stack v3 is the largest, most up-to-date open dataset of source code, crawled directly from GitHub and built to pre-train code LLMs with full-repository context. It is the successor to The Stack v2 and, like its predecessor, is released to make the training… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceCode/stack-v3-train.
Key Features 🔑 1 million+ trajectories from 100 robots, with a total duration of 2976.4 hours. 100+ real-world scenarios across 5 target domains. Cutting-edge hardware: visual tactile sensors / 6-DoF dexterous hand / mobile dual-arm robots 200+ types of tasks: Contact-rich manipulation Long-horizon planning Multi-robot collaboration 87 types of Atomic Skills, including Tie, OpenJar, Peel, Sweep etc. Your… See the full description on the dataset page: https://huggingface.co/datasets/agibot-world/AgiBotWorld-Beta.
Complete Wikipedia dump across all languages. Standard pretraining data source. Structured articles with metadata.
GPIC: A Giant Permissive Image Corpus for Visual Generation Keshigeyan Chandrasegaran*1, Kyle Sargent*1, Suchir Agarwal1, Michael Jang1, Michael Poli1,2, Juan Carlos Niebles1,4, Justin Johnson3, Jiajun Wu1, Li Fei-Fei1 1 Stanford University 2 Radical Numerics 3 University of Michigan 4 Salesforce… See the full description on the dataset page: https://huggingface.co/datasets/stanford-vision-lab/gpic.