Model Hub
Browse PQC-verified AI models, datasets, and tools
Introduction Welcome to MLAAD: The Multi-Language Audio Anti-Spoofing Dataset -- a dataset to train, test and evaluate audio deepfake detection. See the paper for more information. License MLAAD is published strictly for non-commercial academic research use, under the CC-BY-NC 4.0 license. Commercial use is not permitted. Download the dataset Option 1: Hugging Face datasets library Install the datasets package: pip install… See the full description on the dataset page: https://huggingface.co/datasets/mueller91/MLAAD.
ACE-Data-0 Human-Centric Ambient Capture as Embodied Data Engine S-Lab, Nanyang Technological University, Singapore · ACE Robotics ACE turns real home environments into spatially calibrated, temporally synchronized recording studios for embodied AI. ▶ Demo video · Full story, figures, and interactive examples on the blog What this is Learning to act in the physical… See the full description on the dataset page: https://huggingface.co/datasets/ACERobotics/ACE-Data-0.
Dataset Card for NatureBench NatureBench is a cross-discipline benchmark of 27 tasks distilled from peer-reviewed Nature-family publications, spanning 6 scientific domains. It is designed to evaluate whether AI coding agents can move beyond reproduction toward discovery: each task asks an agent to solve a real scientific machine-learning problem and is scored against the source paper's reported state of the art. 📄 arXiv paper: https://arxiv.org/abs/2606.24530 💻 GitHub code… See the full description on the dataset page: https://huggingface.co/datasets/JoeLiu996/NatureBench.
InternData-A1 InternData-A1 is a hybrid synthetic-real manipulation dataset containing over 630k trajectories and 7,433 hours across 4 embodiments, 18 skills, 70 tasks, and 227 scenes, covering rigid, articulated, deformable, and fluid-object manipulation. Your browser does not support the video tag. Your browser does not support the video tag.… See the full description on the dataset page: https://huggingface.co/datasets/InternRobotics/InternData-A1.
Dataset Card for SQuAD Dataset Summary Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles. Supported Tasks and Leaderboards Question Answering.… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad.
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.
FineWeb Tokenized > 4 trillion tokens of the pre-tokenized data the 🌐 web has to offer What is it? This is a pre-tokenized version of the HuggingFaceFW/fineweb dataset (currently in-progress, tokenization of the ~15 trillion tokens corpus is ongoing). The data is being pre-processed and tokenized using the AnisoleAI BPE tokenizer (52,022 vocabulary size) and packed into compact uint16 Parquet shards. By distributing the pre-tokenized corpus, we eliminate… See the full description on the dataset page: https://huggingface.co/datasets/anisoleai/fineweb-tokenized.
🍃 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.
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.
🎞️ Seedance-2-prompts-datasets 🎞️ The ultimate Seedance-2 video prompt dataset (50GB+). 8100+ video generation prompts with full metadata and preview frames. Truly open source: No login, no ads, no redirection. Just pure data for AI video creators. This project is a massive collection of prompts used for Bytedance's Seedance 2.0 and the resulting generated videos. The entire dataset exceeds 50GB and contains 8100+ videos, all structured into a comprehensive dataset. Due… See the full description on the dataset page: https://huggingface.co/datasets/GokuScraper/seedance-2-prompts-datasets.