Model Hub

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.

Egocentric-100K is the largest dataset of manual labor. You can visualize the dataset here. Egocentric-100K is state-of-the-art in hand visibility and active manipulation density compared to previous in-the-wild egocentric datasets. The complete 30,000 frame evaluation set is available at Egocentric-100K-Evaluation. Dataset Statistics Attribute Value Total Hours 100,405 Total Frames 10.8 billion Video Clips 2,010,759 Median Clip Length 180.0 seconds Mean… See the full description on the dataset page: https://huggingface.co/datasets/builddotai/Egocentric-100K.

S2ORC Full — Semantic Scholar Open Research Corpus A complete redistribution of the S2ORC dataset in Parquet format on Hugging Face, containing 14.5 million academic papers with full text, structured metadata, and citation information. Dataset Description S2ORC (Semantic Scholar Open Research Corpus) is a general-purpose corpus for NLP and text mining research over scientific papers, originally developed by the Allen Institute for AI. This version provides the full… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/s2orc_full.

Dataset Card for IFEval Dataset Summary This dataset contains the prompts used in the Instruction-Following Eval (IFEval) benchmark for large language models. It contains around 500 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times" which can be verified by heuristics. To load the dataset, run: from datasets import load_dataset ifeval = load_dataset("google/IFEval") Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/google/IFEval.

Dataset Card for PAWS: Paraphrase Adversaries from Word Scrambling Dataset Summary PAWS: Paraphrase Adversaries from Word Scrambling This dataset contains 108,463 human-labeled and 656k noisily labeled pairs that feature the importance of modeling structure, context, and word order information for the problem of paraphrase identification. The dataset has two subsets, one based on Wikipedia and the other one based on the Quora Question Pairs (QQP) dataset. For further… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/paws.

Dataset Card for "ArtifactAI/arxiv_s2orc_parsed" Dataset Description https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed Dataset Summary AlgorithmicResearchGroup/arxiv_s2orc_parsed is a subset of the AllenAI S2ORC dataset, a general-purpose corpus for NLP and text mining research over scientific papers, The dataset is filtered strictly for ArXiv papers, including the full text for each paper. Github links have been extracted from each… See the full description on the dataset page: https://huggingface.co/datasets/AlgorithmicResearchGroup/arxiv_s2orc_parsed.

Dataset Card for "BrightData/Goodreads-Books" Dataset Summary Explore a collection of millions of books with the Goodreads dataset, comprising over 6.3M structured records and 14 data fields updated and refreshed regularly. Each entry includes all major data points such as URLs, book IDs, titles, authors, ratings, number of ratings, reviews, summaries, genres, publication dates, author details and prices. For a complete list of data points, please refer to the full "Data… See the full description on the dataset page: https://huggingface.co/datasets/BrightData/Goodreads-Books.

PQC Byzantine fault-tolerant consensus for federated AI governance. ML-DSA-65 signed proposals and votes, weighted quorum policy (PBFT 2/3+2/3 default), Byzantine double-vote detection, AuthorizationChain with AUTHORIZE/REVOKE semantics. 9 proposal kinds for enterprise AI oversight. Quantum-resistant DAO for distributed AI trust. 38 tests passing.