datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mbpp
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.paws
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.natural_questions
Dataset Card for Natural Questions
Dataset Summary
The NQ corpus contains questions from real users, and it requires QA systems to
read and comprehend an entire Wikipedia article that may or may not contain the
answer to the question. The inclusion of real user questions, and the
requirement that solutions should read an entire page to find the answer, cause
NQ to be a more realistic and challenging task than prior QA datasets.
Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/natural_questions.gaia2
Gaia2
Paper | Code | Project Page
Dataset Summary
Gaia2 is a benchmark dataset for evaluating AI agent capabilities in simulated environments. The dataset contains 800 scenarios that test agent performance in environments where time flows continuously and events occur dynamically.
The dataset evaluates seven core capabilities: Execution (multi-step planning and state changes), Search (information gathering and synthesis), Adaptability (dynamic response to environmental… See the full description on the dataset page: https://huggingface.co/datasets/meta-agents-research-environments/gaia2.synthesized_datasetnq_open
Dataset Card for nq_open
Dataset Summary
The NQ-Open task, introduced by Lee et.al. 2019,
is an open domain question answering benchmark that is derived from Natural Questions.
The goal is to predict an English answer string for an input English question.
All questions can be answered using the contents of English Wikipedia.
Supported Tasks and Leaderboards
Open Domain Question-Answering,
EfficientQA Leaderboard:… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/nq_open.ResearchClawBench
ResearchClawBench
Evaluating AI Agents for Automated Research from Re-Discovery to New-Discovery
Quick Start | Submit Tasks | How It Works | Domains | Leaderboard | Add Your Agent
ResearchClawBench is a benchmark that measures whether AI coding agents can independently conduct scientific research — from reading raw data to producing publication-quality reports — and then rigorously evaluates the results against real human-authored papers.… See the full description on the dataset page: https://huggingface.co/datasets/InternScience/ResearchClawBench.HumanGengaia2_filesystem
GAIA2 Filesystem
This is a dataset containing files for the GAIA2 benchmark. You should not use this dataset on its own, but instead use the Meta Agents Research Environments framework to execute scenarios from that GAIA2 dataset.
Dataset Link
https://huggingface.co/datasets/meta-agents-research-environments/gaia2
Contact Details
Publishing POC: Meta AI Research Team
Affiliation: Meta Platforms, Inc.
Website:… See the full description on the dataset page: https://huggingface.co/datasets/meta-agents-research-environments/gaia2_filesystem.OmniRooms
UniSHARP:
Universal Sharp Monocular View Synthesis
Meixi Song1 ·
Dizhe Zhang1,* ·
Hao Ren1 ·
Ruiyang Zhang1 ·
Bo Du2 ·
Ming-Hsuan Yang3 ·
Lu Qi1,2,*
1Insta360 Research · 2Wuhan University · 3University of California, Merced
UniSHARP extends SHARP-style photorealistic monocular view synthesis to universal camera systems. Given a single image from a perspective, wide-FoV, fisheye, or panoramic camera, UniSHARP predicts a 3D Gaussian representation and… See the full description on the dataset page: https://huggingface.co/datasets/Insta360-Research/OmniRooms.ResearchData_P1qa_squadshifts_synthetic_randomTBA
yayi2_pretrain_data
介绍/Introduction
本数据集源自雅意训练语料，我们精选了约100B数据，数据大小约为500GB。我们期望通过雅意预训练数据的开源推动中文预训练大模型开源社区的发展，并积极为此贡献力量。通过开源，我们与每一位合作伙伴共同构建雅意大模型生态。
We opensource the pre-trained dataset in this release, it should contain more than 100B tokens depending on the tokenizer you use, requiring more than 500GB of local storage. By open-sourcing the pre-trained dataset, we aim to contribute to the development of the Chinese pre-trained large language model open-source community. Through open-source, we aspire to… See the full description on the dataset page: https://huggingface.co/datasets/wenge-research/yayi2_pretrain_data.tydiqa
Dataset Card for "tydiqa"
Dataset Summary
TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language
expresses -- such that we expect models performing well on this set to generalize across a large number of the languages
in the world. It contains language phenomena that would not be found in… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/tydiqa.research-papers
research-papers Dataset
Overview
The Research Papers Dataset is a collection of academic research documents categorized by their primary research topic.
This dataset is designed for tasks such as model finetuning, document classification, optical character recognition (OCR) testing and multimodal document understanding (Feel free to use it however you see fit!).
Curated by: tegridy
Language: English
Format: PDF | MD
Repo Structure
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/tegridydev/research-papers.go_emotions
Dataset Card for GoEmotions
Dataset Summary
The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral.
The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test
splits.
Supported Tasks and Leaderboards
This dataset is intended for multi-class, multi-label emotion classification.
Languages
The data is in English.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/go_emotions.kaz-vision-50kconceptual_captions
Dataset Card for Conceptual Captions
Dataset Summary
Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/conceptual_captions.siku-ming-qing-biejihilti-slam-challenge-2022
Hilti SLAM Challenge 2022
The Hilti SLAM Challenge 2022 dataset is a multimodal robotics benchmark for evaluating simultaneous localization and mapping (SLAM) and sensor-fusion systems in challenging real-world environments.
The dataset was recorded with the handheld Phasma sensor platform at an active construction site in Schaan, Liechtenstein, and at the historic Sheldonian Theatre in Oxford, United Kingdom. The sequences include multi-level construction environments… See the full description on the dataset page: https://huggingface.co/datasets/Hilti-Research/hilti-slam-challenge-2022.AmericanStoriesAmerican Stories offers high-quality structured data from historical newspapers suitable for pre-training large language models to enhance the understanding of historical English and world knowledge. It can also be integrated into external databases of retrieval-augmented language models, enabling broader access to historical information, including interpretations of political events and intricate details about people's ancestors. Additionally, the structured article texts facilitate the application of transformer-based methods for popular tasks like detecting reproduced content, significantly improving accuracy compared to traditional OCR methods. American Stories serves as a substantial and valuable dataset for advancing multimodal layout analysis models and other multimodal applications.essential-web-1t-sample-fdc-partitioned
🌐 Essential-Web: FDC Level-2 Partitioned Dataset
📋 Dataset Description
This dataset contains a 1 trillion token sample from Essential-Web, partitioned by Free Decimal Correspondence (FDC) level-2 categories. Essential-Web is a 24-trillion-token web dataset with extensive document-level metadata designed to enable rapid dataset curation through SQL-like filtering.
🔍 Free Decimal Correspondence (FDC)
The FDC taxonomy is an open classification system… See the full description on the dataset page: https://huggingface.co/datasets/Research-EAI/essential-web-1t-sample-fdc-partitioned.pa-warm-start-sft-heavy-25b-mix
geodesic-research/pa-warm-start-sft-heavy-25b-mix
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/pa-warm-start-sft-heavy-25b-mix", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/pa-warm-start-sft-heavy-25b-mix.paws-x
Dataset Card for PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification
Dataset Summary
This dataset contains 23,659 human translated PAWS evaluation pairs and
296,406 machine translated training pairs in six typologically distinct
languages: French, Spanish, German, Chinese, Japanese, and Korean. All
translated pairs are sourced from examples in
PAWS-Wiki.
For further details, see the accompanying paper:
PAWS-X: A Cross-lingual Adversarial Dataset for… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/paws-x.benchmark-research
Benchmark Research
面向金融 Deep Research Agent 的评测数据集（仅数据）。
代码、builder、prompt 模板与评测脚本在独立代码仓库维护；本 Hub 仓库只发布标准化 ready seeds，支持按子集按需加载。
快速开始
from datasets import load_dataset
# 按题型子集加载（推荐）
a1 = load_dataset("sselaine27/benchmark-research", "a1")
c = load_dataset("sselaine27/benchmark-research", "c")
d = load_dataset("sselaine27/benchmark-research", "d")
print(a1["train"][0]["task_id"])
print(a1["train"][0]["prompt"][:200])
# 按时间层筛选
t2 = a1["train"].filter(lambda x:… See the full description on the dataset page: https://huggingface.co/datasets/sselaine27/benchmark-research.regent-subset-of-jat-dataset-tokenizedThis is the dataset for REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context In New Environments.
The REGENT dataset includes a subset of the JAT (tokenized) dataset (from https://huggingface.co/datasets/jat-project) for the REGENT training environments.
It has around a 100k transitions from each of the 145 training environments (45 metaworld, 52 atari, 9 mujoco, 39 babyai).
Please find this in the *_subset folders.
It also has distance values for input sequences used in… See the full description on the dataset page: https://huggingface.co/datasets/regent-research/regent-subset-of-jat-dataset-tokenized.control-pretraining-datasets-smoke
geodesic-research/control-pretraining-datasets-smoke
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/control-pretraining-datasets-smoke", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/control-pretraining-datasets-smoke.acp_bench
ACP Bench
🏠 Homepage •
📄 Paper •
📄 Paper
ACPBench is a benchmark dataset designed to evaluate the reasoning capabilities of large language models (LLMs) in the context of Action, Change, and Planning. It spans 13 diverse domains:
Blocksworld
Logistics
Grippers
Grid
Ferry
FloorTile
Rovers
VisitAll
Depot
Goldminer
Satellite
Swap
Alfworld
Task Types in ACPBench
ACPBench includes the following 8 reasoning tasks:
Action Applicability (app)… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/acp_bench.hilti-slam-challenge-2023
Hilti SLAM Challenge 2023
The Hilti SLAM Challenge 2023 dataset is a multimodal robotics benchmark for evaluating simultaneous localization and mapping (SLAM) and sensor-fusion systems in challenging, real-world construction environments.
The 2023 challenge extends the previous Hilti SLAM benchmarks to multi-session and multi-platform SLAM. Recordings were collected across multiple active construction sites, with overlapping trajectories captured during different sessions and… See the full description on the dataset page: https://huggingface.co/datasets/Hilti-Research/hilti-slam-challenge-2023.newswire
Dataset Card for NewsWire
Dataset Summary
NewsWire contains 2.7 million unique public domain U.S. news wire articles, written between 1878 and 1977. Locations in these articles are georeferenced, topics are tagged using customized neural topic classification, named entities are recognized, and individuals are disambiguated to Wikipedia using a novel entity disambiguation model.
Languages
English (en)
Dataset Structure
Each year in the dataset is… See the full description on the dataset page: https://huggingface.co/datasets/dell-research-harvard/newswire.