datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.squad_v2squadv2trainsquad-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_v2triviaqa-hotpotqa-nq-squad-msmarco-hard-neg-sft4b-doc4096-seq1024-v2long_squad_v2squad_v2_codex_glue_cnn_dailymail_llama1b_modifiedlong_squad_v2
Dataset Card for long_squad_v2
long_squad_v2 is a long-context question answering dataset based on the SQuAD v2 format. It was constructed by concatenating multiple SQuAD v2 contexts to significantly increase the average document length, enabling training and evaluation of models on long-range understanding and sparse answer retrieval tasks.
Dataset Details
Uses
To load the dataset using the 🤗 Datasets library:
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/huutuan/long_squad_v2.squad_v2squad-nl-v2.0
SQuAD-NL v2.0 [translated SQuAD / XQuAD]
SQuAD-NL v2.0 is a translation of The Stanford Question Answering Dataset (SQuAD) v2.0.
Since the original English SQuAD test data is not public, we reserve the same documents that were used for XQuAD for testing purposes. These documents are sampled from the original dev data split. The English data is automatically translated using Google Translate (February 2023) and the test data is manually post-edited.
This version of SQuAD-NL also… See the full description on the dataset page: https://huggingface.co/datasets/GroNLP/squad-nl-v2.0.squad-v2-closed-corpus-v1
SQuAD 2.0 Closed Wikipedia Corpus v1
Full plaintext for 452 Wikipedia articles drawn from the SQuAD 2.0 source corpus (477 unique articles total; 25 dev articles held back for grading).
Dataset configs
Config
File
Use
default
train.parquet
Dataset Viewer — title, pageid, text length, 1000-char preview
full
corpus_v1.parquet
Full article text via load_dataset(..., name="full")
—
corpus_v1.jsonl
Pinned release file for offset validation (download… See the full description on the dataset page: https://huggingface.co/datasets/bbasavar/squad-v2-closed-corpus-v1.squadv2validationsquadv2_4_5_1234squadv2_4_1_2345harrypotter_squad_v2_2
Dataset Summary
Contains 15 Harry Potter trivia questions in Squadv2 format, 3 of which are unanswerable.
Model Performance
Test Notebook
Model
exact
f1
Albert Base (twmkn9/albert-base-v2-squad2)
46.6667
46.6667
Albert XXLarge (ahotrod/albert_xxlargev1_squad2_512)
66.6667
66.6667
chinese-squadv2English | 中文
Dataset Card for Chinese SQuAD 2.0 (revised, bilingual)
Dataset Description
This is a revised and extended version of the Chinese translation of SQuAD 2.0,
originally machine-translated by
ChineseSquad. Like SQuAD 2.0 it
contains both answerable and unanswerable questions and is designed for Chinese
extractive reading comprehension / question answering.
Compared with the previous release of chinese-squadv2, this version:
Adds the original English… See the full description on the dataset page: https://huggingface.co/datasets/real-jiakai/chinese-squadv2.squad-pairs-hard-neg-reasoning-embedding-modified-SFT-4B-doc4096-seq1024-v2-parts-0-105-squad_v2harrypotter_squad_v2squad_v2_modified_for_t5_qgsquad_v2_factuality_v1
squad_v2_factuality_v1
This dataset is derived from "squad_v2" training "context" with the following steps.
NER is run to extract entities.
Lexicon of person's name, date, organisation name and location are collected.
20% of the time, one of the text attribute (person's name, date, organisation name and location) is randomly replaced. For consistency of context, all other place with the same name is also replaced.
Purpose of the Dataset
The purpose of this dataset… See the full description on the dataset page: https://huggingface.co/datasets/kenhktsui/squad_v2_factuality_v1.squad_v2
Dataset Card for squad_v2
This dataset has been created with Argilla.
As shown in the sections below, this dataset can be loaded into Argilla as explained in Load with Argilla, or used directly with the datasets library in Load with datasets.
Dataset Summary
This dataset contains:
A dataset configuration file conforming to the Argilla dataset format named argilla.yaml. This configuration file will be used to configure the dataset when using the… See the full description on the dataset page: https://huggingface.co/datasets/argilla/squad_v2.squad_v2
Dataset Card for "squad_v2"
More Information needed
squad_v2squad_v2_processedkor_squad_v2
Dataset Card for squad_v2
Licensing Information
The data is distributed under the CC BY SA 4.0 license.
Source Data Citation Information
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid =… See the full description on the dataset page: https://huggingface.co/datasets/KETI-NLP/kor_squad_v2.finbenchv2-squad_v2-fi-mt
Dataset Summary
This is a Finnish SQuAD question answering dataset used in FIN-bench-v2: A Unified and Robust Benchmark Suite for Evaluating Finnish Large Language Models. It is a DeepL-based machine translation of the English SQuAD2.0 dataset which combines the 100,000 questions in
SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible… See the full description on the dataset page: https://huggingface.co/datasets/TurkuNLP/finbenchv2-squad_v2-fi-mt.squad_v2_back_trans_augkor_squad_v2