datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.nq_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.deepsearchqa
DeepSearchQA
A 900-prompt factuality benchmark from Google DeepMind, designed to evaluate agents on difficult multi-step information-seeking tasks across 17 different fields.
▶ Google DeepMind Release Blog Post▶ DeepSearchQA Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code
Benchmark
DeepSearchQA is a 900-prompt benchmark for evaluating agents on difficult multi-step information-seeking tasks across 17 different fields. Unlike traditional… See the full description on the dataset page: https://huggingface.co/datasets/google/deepsearchqa.xtreme
Dataset Card for "xtreme"
Dataset Summary
The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and
2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into
14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese,
Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the… See the full description on the dataset page: https://huggingface.co/datasets/google/xtreme.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.xquad
Dataset Card for "xquad"
Dataset Summary
XQuAD (Cross-lingual Question Answering Dataset) is a benchmark dataset for evaluating cross-lingual question answering
performance. The dataset consists of a subset of 240 paragraphs and 1190 question-answer pairs from the development set
of SQuAD v1.1 (Rajpurkar et al., 2016) together with their professional translations into ten languages: Spanish, German,
Greek, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, and Hindi.… See the full description on the dataset page: https://huggingface.co/datasets/google/xquad.frames-benchmark
FRAMES: Factuality, Retrieval, And reasoning MEasurement Set
FRAMES is a comprehensive evaluation dataset designed to test the capabilities of Retrieval-Augmented Generation (RAG) systems across factuality, retrieval accuracy, and reasoning.
Our paper with details and experiments is available on arXiv: https://arxiv.org/abs/2409.12941.
Dataset Overview
824 challenging multi-hop questions requiring information from 2-15 Wikipedia articles
Questions span diverse topics… See the full description on the dataset page: https://huggingface.co/datasets/google/frames-benchmark.anle-toaan-gov-vn
Vietnamese Án lệ Corpus — anle.toaan.gov.vn
🇻🇳 Tóm tắt. Bộ dữ liệu các bản án + án lệ Việt Nam thu thập từ cổng
anle.toaan.gov.vn của Tòa án nhân dân tối cao.
Mỗi văn bản đi kèm markdown chuẩn hoá tiếng Việt và một lớp grounding mức câu
(mỗi trích dẫn mang sentence_id + char span trỏ ngược vào markdown). Bộ dữ
liệu là một phần của ViLA common-corpus và ship ba cấu hình HF theo chuẩn
chung: documents (bảng chính) · embeddings (vector 4096-D Nemotron-3-Embed-8B) ·
reduces (toạ… See the full description on the dataset page: https://huggingface.co/datasets/tmquan/anle-toaan-gov-vn.adaption-charts-p2-gold
Adaption Charts P2 — Gold Chart-QA Dataset
A verified, quality-first chart question-answering dataset built for the
Adaption Labs AutoScientist Challenge (Part 2, Data Visualization track).
Two sources: a programmatically generated synthetic core
(correct-by-construction) and a hand-authored hardset built from real
public dashboards and reports.
At a glance
3803 rows total — 3705 synthetic + 98 hardset
7 chart types — bar, line, grouped_bar, stacked_bar, pie… See the full description on the dataset page: https://huggingface.co/datasets/vinod-anbalagan/adaption-charts-p2-gold.simpleqa-verified
SimpleQA Verified
A 1,000-prompt factuality benchmark from Google DeepMind and Google Research, designed to reliably evaluate LLM parametric knowledge.
▶ SimpleQA Verified Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code
Benchmark
SimpleQA Verified is a 1,000-prompt benchmark for reliably evaluating Large Language Models (LLMs) on short-form factuality
and parametric knowledge. The authors from Google DeepMind and Google Research… See the full description on the dataset page: https://huggingface.co/datasets/google/simpleqa-verified.mgsm-gold
MGSM Gold - Multilingual Grade School Math
This dataset contains the MGSM (Multilingual Grade School Math) benchmark - 250 math word problems translated into 10 languages.
Attribution
This dataset is derived from juletxara/mgsm
Original source: google-research/url-nlp/mgsm
Usage
from datasets import load_dataset
# Load German test set
dataset = load_dataset("alibashir/mgsm-gold", "de")
print(dataset["test"][0])
Languages
Code
Language… See the full description on the dataset page: https://huggingface.co/datasets/alibashir/mgsm-gold.FACTS-grounding-public
FACTS Grounding 1.0 Public Examples
860 public FACTS Grounding examples from Google DeepMind and Google Research
FACTS Grounding is a benchmark from Google DeepMind and Google Research designed to measure the performance of AI Models on factuality and grounding.
▶ FACTS Grounding Leaderboard on Kaggle▶ Technical Report▶ Evaluation Starter Code▶ Google DeepMind Blog Post
Usage
The FACTS Grounding benchmark evaluates the ability of Large Language Models (LLMs)… See the full description on the dataset page: https://huggingface.co/datasets/google/FACTS-grounding-public.spiqa
SPIQA Dataset Card
Dataset Details
Dataset Name: SPIQA (Scientific Paper Image Question Answering)
Paper: SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers
Github: SPIQA eval and metrics code repo
Dataset Summary: SPIQA is a large-scale and challenging QA dataset focused on figures, tables, and text paragraphs from scientific research papers in various computer science domains. The figures cover a wide variety of plots… See the full description on the dataset page: https://huggingface.co/datasets/google/spiqa.phapdien-moj-gov-vn
Bộ Pháp Điển Việt Nam — phapdien.moj.gov.vn
🇻🇳 Tóm tắt. Bộ ngữ liệu cấp Điều của Bộ Pháp Điển Việt Nam — bộ pháp điển
chính thức do Bộ Tư pháp công bố. Mỗi dòng documents là một Điều kèm toàn văn đã
chuẩn hoá, chương sở thuộc, đề mục và chủ đề. Kèm theo là vector nhúng ngữ nghĩa 4096-D
(embeddings), toạ độ giảm chiều trong không gian chung ViLA (reduces), và từ điển
ontology song ngữ Việt–Anh (chủ đề · đề mục · thuật ngữ).
🇬🇧 One-line. Article-level corpus of the Bộ Pháp… See the full description on the dataset page: https://huggingface.co/datasets/tmquan/phapdien-moj-gov-vn.unpredictable_support-google-comThe UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.gspc-gov
GSPC — governance bank (GovBench)
Bank (governance). Frozen split. Live n is the governance row on GET https://councilof.ai/api/gspc, not a Hub leaderboard score. Not a certificate.
Art 50 dates (EUR-Lex): 2 August 2026 live; marking grace 2 December 2026.
Council of AI measurement bank. Measurement, not certification.
Live measurement. This bank stands behind the governance row of the live GSPC board: GET https://councilof.ai/api/gspc?axis=governance (family, kind, status and… See the full description on the dataset page: https://huggingface.co/datasets/csoai/gspc-gov.reddit_dataset_2025
Bittensor Subnet 13 Reddit Dataset
Miner Data Compliance Agreement
In uploading this dataset, I am agreeing to the Macrocosmos Miner Data Compliance Policy.
Dataset Summary
This dataset is part of the Bittensor Subnet 13 decentralized network, containing preprocessed Reddit data. The data is continuously updated by network miners, providing a real-time stream of Reddit content for various analytical and machine learning tasks.
For more… See the full description on the dataset page: https://huggingface.co/datasets/goldentraversy07/reddit_dataset_2025.cbba-toaan-gov-vn
Vietnamese Bản án Corpus — congbobanan.toaan.gov.vn
🇻🇳 Tóm tắt. Bản án sơ thẩm/phúc thẩm/giám đốc thẩm/tái thẩm của Việt Nam,
thu thập từ cổng công bố bản án
congbobanan.toaan.gov.vn của Tòa án nhân
dân tối cao. Ba cấu hình HF khoá theo doc_name/id: documents (nội dung
siêu dữ liệu + trích dẫn), embeddings (vector 4096-D Nemotron-3-8B),
reduces (toạ độ t-SNE/UMAP trong không gian chung 6 bộ dữ liệu). Tên
cột và giá trị phân loại bằng tiếng Anh; chỉ nội dung pháp lý giữ tiếng… See the full description on the dataset page: https://huggingface.co/datasets/tmquan/cbba-toaan-gov-vn.gspc-jail-goldbank
GSPC — jail bank (GoldBank-Detector)
Council of AI measurement bank. Measurement, not certification.
Bank. Frozen split. Live n is the matching axis on GET https://councilof.ai/api/gspc, not a Hub score. Not a certificate. Art 50 (EUR-Lex): 2 August 2026 live; marking grace 2 December 2026.
Live measurement. This bank stands behind the jail row of the live GSPC board: GET https://councilof.ai/api/gspc?axis=jail (family, kind, status and n are on that row, never typed here; the… See the full description on the dataset page: https://huggingface.co/datasets/csoai/gspc-jail-goldbank.cfq
Dataset Card for "cfq"
Dataset Summary
The Compositional Freebase Questions (CFQ) is a dataset that is specifically designed to measure compositional
generalization. CFQ is a simple yet realistic, large dataset of natural language questions and answers that also
provides for each question a corresponding SPARQL query against the Freebase knowledge base. This means that CFQ can
also be used for semantic parsing.
Supported Tasks and Leaderboards
More Information… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/cfq.unpredictable_support-google-comThe UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.WikiProfile
WikiProfile
WikiProfile is a factual knowledge benchmark for evaluating how well language models encode and recall factual knowledge. It comprises 2,150 facts, each paired with 10 questions, for a total of 21,500 question instances.
Each fact is grounded in the first paragraph (summary) of an English Wikipedia page and is defined as a proposition between two entities, a subject and an object (e.g., "Oasis played their first gig at the Boardwalk club" → subject: Oasis, object:… See the full description on the dataset page: https://huggingface.co/datasets/google/WikiProfile.dichvucong-gov-vn
Vietnam Administrative Procedures — full structured detail (dichvucong.gov.vn)
🇻🇳 Tóm tắt. 3,927 thủ tục hành chính từ Cổng Dịch vụ công
Quốc gia, mỗi thủ tục kèm toàn bộ nội dung có cấu trúc: trình tự, cách
thức, thành phần hồ sơ, phí/lệ phí, căn cứ pháp lý, kết quả, cơ quan thực
hiện. Kèm embedding + toạ độ UMAP/PCA/t-SNE và một báo cáo phân tích sâu.
🇬🇧 Summary. 3,927 Vietnamese administrative procedures from the
National Public Service Portal, each with the full… See the full description on the dataset page: https://huggingface.co/datasets/tmquan/dichvucong-gov-vn.xquad_r
Dataset Card for [Dataset Name]
Dataset Summary
XQuAD-R is a retrieval version of the XQuAD dataset (a cross-lingual extractive
QA dataset). Like XQuAD, XQUAD-R is an 11-way parallel dataset, where each
question appears in 11 different languages and has 11 parallel correct answers
across the languages.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
The dataset can be found with the following languages:
Arabic: xquad-r/ar.json… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/xquad_r.disfl_qa
Dataset Card for DISFL-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Dataset Summary
Disfl-QA is a targeted dataset for contextual disfluencies in an information seeking setting, namely question answering over Wikipedia passages. Disfl-QA builds upon the SQuAD-v2 (Rajpurkar et al., 2018) dataset, where each question in the dev set is annotated to add a contextual disfluency using the paragraph as a source of distractors.
The final dataset… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/disfl_qa.aquamuse
Dataset Card for AQuaMuSe
Dataset Summary
AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)
This dataset contains versions of automatically generated datasets for abstractive and extractive query-based multi-document summarization as described in AQuaMuSe paper.… See the full description on the dataset page: https://huggingface.co/datasets/google-research-datasets/aquamuse.hle-gold-bio-chem
Humanity's Last Exam (HLE) Bio/Chem Gold
Humanity’s Last Exam (HLE) is a challenging question-answering AI benchmark covering advanced academic fields including Math, Physics, Chemistry, Biology, Engineering, and Computer Science.
At FutureHouse, we audited the biology and chemistry subsets of HLE using a combination of expert human evaluators and our in-house research agent, and found that around 30% of the questions contain answers directly contradicted by peer-reviewed… See the full description on the dataset page: https://huggingface.co/datasets/futurehouse/hle-gold-bio-chem.goat
Dataset Card for Dataset Name
Dataset Summary
The dataset.json file contains ~1.7 million synthetic data for arithmetic tasks, generated by dataset.ipynb.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information Needed]
Data Splits
[More Information Needed]
Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/tiedong/goat.turkish-extractive-qa-1.5m
Turkish Extractive QA 1.5M v2
Cevap metni ve başlangıç konumu doğrulanabilir Türkçe çıkarımsal soru-cevap kayıtları.
Doğrulanmış boyut
Train: 1,470,000
Validation: 15,000
Test: 15,000
Toplam: 1,500,000
Ana görev sütunları: id, context, question, answer, answer_start, question_type
Provenance
Veri insan mesajlarından, belgelerinden veya web kazımasından alınmamıştır. Tamamı
depodaki üretici koduyla deterministik olarak oluşturulur. Her satırda… See the full description on the dataset page: https://huggingface.co/datasets/GoktugD/turkish-extractive-qa-1.5m.GooseReason-0.7M
Xent GooseReason 0.7M
This is a reproducibly shuffled and benchmark-decontaminated derivative of
nvidia/Nemotron-Research-GooseReason-0.7M.
Splits
Each source subset has a training split plus validation and test splits containing
500 rows each. Holdouts are stratified by num_choices with largest-remainder
allocation, then every split is deterministically shuffled with seed 42.
Source subset
Original
Raw exact
Added by normalization
Invalid/mask removed… See the full description on the dataset page: https://huggingface.co/datasets/xent-labs/GooseReason-0.7M.