datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
squad
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… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad.squad_v2
Dataset Card for SQuAD 2.0
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 2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers… See the full description on the dataset page: https://huggingface.co/datasets/rajpurkar/squad_v2.SQuADDS_Layouts
SQuADDS Layouts - versioned GDS artifacts for superconducting quantum hardware
SQuADDS Layouts is the geometry-artifact companion to
SQuADDS_DB, the
Superconducting Qubit And Device Design and Simulation Database. It provides
checksum-verified GDS files, stable geometry identities, and machine-readable
geometry metadata so a simulation result can be traced to the exact layout
that produced it.
Homepage: https://lfl-lab.github.io/SQuADDS/
Repository:… See the full description on the dataset page: https://huggingface.co/datasets/SQuADDS/SQuADDS_Layouts.based-squadPlease consider citing the following if you use this task in your work:
@inproceedings{Rajpurkar2018SQuAD2,
title={Know What You Don't Know: Unanswerable Questions for SQuAD},
author={Pranav Rajpurkar and Jian Zhang and Percy Liang},
booktitle={ACL 2018},
year={2018}
}
@article{arora2024simple,
title={Simple linear attention language models balance the recall-throughput tradeoff},
author={Arora, Simran and Eyuboglu, Sabri and Zhang, Michael and Timalsina, Aman and Alberti, Silas… See the full description on the dataset page: https://huggingface.co/datasets/hazyresearch/based-squad.squad-gen2mcsquadsquad
Dataset Card for SQuAD
This dataset is a collection of question-answer pairs from the SQuAD dataset. See SQuAD for additional information.
This dataset can be used directly with Sentence Transformers to train embedding models.
Dataset Subsets
pair subset
Columns: "question", "answer"
Column types: str, str
Examples:{
'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
'answer': 'Architecturally, the school has a Catholic… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/squad.squad_v2squad_kor_v1
Dataset Card for KorQuAD v1.0
Dataset Summary
KorQuAD 1.0 is a large-scale question-and-answer dataset constructed for Korean machine reading comprehension, and investigate the dataset to understand the distribution of answers and the types of reasoning required to answer the question. This dataset benchmarks the data generating process of SQuAD v1.0 to meet the standard.
Supported Tasks and Leaderboards
question-answering
Languages
Korean… See the full description on the dataset page: https://huggingface.co/datasets/KorQuAD/squad_kor_v1.spoken-squad-t2aTriviaQA-in-SQuAD-format
Dataset Card for "TriviaQA-in-SQuAD-format"
More Information needed
squad-conteb-train
ConTEB - SQuAD (training)
This dataset is part of ConTEB (Context-aware Text Embedding Benchmark), designed for evaluating contextual embedding model capabilities. It stems from the widely used SQuAD dataset.
Dataset Summary
SQuAD is an extractive QA dataset with questions associated to passages and annotated answer spans, that allow us to chunk individual passages into shorter sequences while preserving the original annotation. To build the corpus, we start from the… See the full description on the dataset page: https://huggingface.co/datasets/illuin-conteb/squad-conteb-train.pixel_squad
Dataset Card for "pixel_squad"
More Information needed
iapp_wiki_qa_squad
iapp_wiki_qa_squad
Extractive question answering over Thai Wikipedia articles, in SQuAD format.
7,242 questions across 1,912 articles, annotated by people iApp hired for the purpose.
from datasets import load_dataset
dataset = load_dataset("iapp/iapp_wiki_qa_squad")
This works again as of the August 2026 revision. Until then it did not. The
repository carried a loading script and no data, and datasets dropped script support
at v3, so load_dataset failed and every… See the full description on the dataset page: https://huggingface.co/datasets/iapp/iapp_wiki_qa_squad.squad-conteb-eval
ConTEB - SQuAD (evaluation)
This dataset is part of ConTEB (Context-aware Text Embedding Benchmark), designed for evaluating contextual embedding model capabilities. It stems from the widely used SQuAD dataset.
Dataset Summary
SQuAD is an extractive QA dataset with questions associated to passages and annotated answer spans, that allow us to chunk individual passages into shorter sequences while preserving the original annotation. To build the corpus, we start from the… See the full description on the dataset page: https://huggingface.co/datasets/illuin-conteb/squad-conteb-eval.spoken_squad_testThis dataset is licensed under the terms of the CC-BY-SA-4.0 license.
https://github.com/Chia-Hsuan-Lee/Spoken-SQuAD/blob/master/LICENSE.md
Author: @michaellee886
@article{li2018spoken,
title={Spoken SQuAD: A study of mitigating the impact of speech recognition errors on listening comprehension},
author={Li, Chia-Hsuan and Wu, Szu-Lin and Liu, Chi-Liang and Lee, Hung-yi},
journal={arXiv preprint arXiv:1804.00320},
year={2018}
}
@article{wang2024audiobench,
title={AudioBench: A… See the full description on the dataset page: https://huggingface.co/datasets/AudioLLMs/spoken_squad_test.squad_kor_v1squad_it
Dataset Card for "squad_it"
Dataset Summary
SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
split into training and test sets to support the replicability of the… See the full description on the dataset page: https://huggingface.co/datasets/crux82/squad_it.COLO
COw LOcalization (COLO) Dataset
The COw LOcalization (COLO) dataset is designed to localize cows in various indoor environments using different lighting conditions and view angles. This dataset offers 1,254 images and 11,818 cow instances, serving as a benchmark for the precision livestock farming community.
Dataset Configurations
Configuration
Training Split
Testing Split
0_all
Top-View + Side-View
Top-View + Side-View
1_top
Top-View
Top-View… See the full description on the dataset page: https://huggingface.co/datasets/Niche-Squad/COLO.ls-squad
ls-squad
This dataset contains the files necessary to view in latentscope.
The files in the latentscope are used by the app to view.
Total size of dataset files: 541.6 MB.
To view this scope, pip install latentscope and run ls-serve. You can then download via the UI and explore.
If you have questions, join us on discord.
squadv2trainsquad_v1_pt
Dataset Card for "squad_v1_pt"
Dataset Summary
Portuguese translation of the SQuAD dataset. The translation was performed automatically using the Google Cloud API.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
default
Size of downloaded dataset files: 39.53 MB
Size of the generated dataset: 96.72 MB
Total amount of disk used: 136.25 MB
An… See the full description on the dataset page: https://huggingface.co/datasets/nunorc/squad_v1_pt.SQuADDS_Layout_Embeddings
SQuADDS Layout Embeddings
Versioned layout representations for the 24,106 GDS artifacts in
SQuADDS/SQuADDS_Layouts.
Static embedding model v0
static-embedding-v0 implements the original SQuADDS proof-of-concept model:
v0 = parameter_sum + geometric_moments + flattened_shape_bitmap
Each unit-normalized vector has 9,227 dimensions:
Block
Dimensions
Contents
Parameter sum
1
Permutation- and parameter-count-invariant sum of numerical design options… See the full description on the dataset page: https://huggingface.co/datasets/SQuADDS/SQuADDS_Layout_Embeddings.squad-v2-dutch
SQuAD v2 Dutch (MTEB retrieval format)
Dutch general-knowledge (Wikipedia) retrieval task. Given a Dutch question, retrieve the relevant Wikipedia passage from the corpus. Built from the validation split, answerable questions only.
Reformatted into MTEB retrieval format from yhavinga/squad_v2_dutch, a machine translation of SQuAD v2.
License: CC BY-SA 4.0.
squad-v1.1-t5-question-generation
Dataset Card for "squad-v1.1-t5-question-generation"
Dataset Summary
This is a modified Stanford Question Answering Dataset (SQuAD) to suit question generation with All Questions in One Line (AQOL) just like in Transformer-based End-to-End Question Generation
specifically for the T5 family of models. The prefix is generate questions: so that the task can be unique to a trained model.
Check out the generation notebook here.
Supported Tasks and Leaderboards… See the full description on the dataset page: https://huggingface.co/datasets/derek-thomas/squad-v1.1-t5-question-generation.triviaqa-hotpotqa-nq-squad-msmarco-hard-neg-sft4btask074_squad1.1_question_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task074_squad1.1_question_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task074_squad1.1_question_generation.tamil_squad-2.0squad-pt-v1.1
Dataset Card for "squad-pt-v1.1"
Dataset squad-v1.1 traduzido pelo grupo (www.deeplearningbrasil.com.br). Todos os créditos ao grupo pela tradução e aos autores originais.
task075_squad1.1_answer_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task075_squad1.1_answer_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task075_squad1.1_answer_generation.