Model Hub
Browse PQC-verified AI models, datasets, and tools
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 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.
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 OpenAI HumanEval Dataset Summary The HumanEval dataset released by OpenAI includes 164 programming problems with a function sig- nature, docstring, body, and several unit tests. They were handwritten to ensure not to be included in the training set of code generation models. Supported Tasks and Leaderboards Languages The programming problems are written in Python and contain English natural text in comments and docstrings.… See the full description on the dataset page: https://huggingface.co/datasets/openai/openai_humaneval.
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.
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.
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.