datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
quantum-representations
Epsilon-Transformers Belief Analysis Dataset
This dataset contains trained neural network models and their corresponding belief state regression analysis from the Epsilon-Transformers project. The models were trained on four different stochastic processes and analyzed for their ability to learn and represent belief states.
See https://github.com/adamimos/epsilon-transformers/tree/quantum-public for codebase which generated this data.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/SimplexAI/quantum-representations.SimpleQA
SimpleQA
A factuality benchmark called SimpleQA that measures the ability for language models to answer short, fact-seeking questions.
Sources
openai/simple-evals
Introducing SimpleQA
Measuring short-form factuality in large language models
SimpleSafetyTestssimpleqa-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.simplified_grooveThis is a copy of the Magenta Groove dataset
The script ´simplify_midi_pretty.py` reads the midi data and simplifies it, by removing any midi values that aren't kicks or snares, and quantizing the notes.
SimpleQA-VerifiedSimpleQA 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 address various limitations of SimpleQA, originally designed by Wei et al. (2024) at OpenAI, including noisy and incorrect labels, topical biases, and question redundancy. SimpleQA Verified was created to provide the research community with a more precise instrument to track genuine progress in… See the full description on the dataset page: https://huggingface.co/datasets/codelion/SimpleQA-Verified.SimpleQA-VerifiedSimpleQA 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 address various limitations of SimpleQA, originally designed by Wei et al. (2024) at OpenAI, including noisy and incorrect labels, topical biases, and question redundancy.
SimpleQA Verified was created to provide the research community with a more precise instrument to track genuine progress in… See the full description on the dataset page: https://huggingface.co/datasets/stalkermustang/SimpleQA-Verified.amazon_zh_simplewikipedia-22-12-simple-embeddings
wikipedia-22-12-simple-embeddings
A modified version of Cohere/wikipedia-22-12-simple-embeddings
meant for use with PostgreSQL with pgvector and Timescale Vector.
Dataset Details
This dataset was created for exploring time-based filtering and semantic search in PostgreSQL with pgvector and Timescale Vector.
This is a modified version of the Cohere wikipedia-22-12-simple-embeddings dataset hosted on Huggingface.
It contains embeddings of Simple English Wikipedia… See the full description on the dataset page: https://huggingface.co/datasets/timescale/wikipedia-22-12-simple-embeddings.simplifyweibo_4_moodsSimpleToolCallingsimple_english_wikipediaOnly for the reaseaching usage.
The original data from http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz.
We use nq_distilbert-base-v1 model encode all the data to the PyTorch Tensors. And normalize the embeddings by using sentence_transformers.util.normalize_embeddings.
How to use
See notebook Wikipedia Q&A Retrieval-Semantic Search
Installing the package
!pip install sentence-transformers==2.3.1
The converting process
# the whole process takes… See the full description on the dataset page: https://huggingface.co/datasets/aisuko/simple_english_wikipedia.simple-cooccurrence-biassimple-cooccurrence-bias contains the sentences used in the simple cooccurrence (or association) test used for evaluating gender bias originally proposed for GPT-3 (Brown et al., 2020).
This dataset is based on the details described in "Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model" (Smith et al., 2022).
simplification-datasetДанный dataset был собран из корпуса "RuSimpleSentEval" (https://github.com/dialogue-evaluation/RuSimpleSentEval), а также "RuAdapt" (https://github.com/Digital-Pushkin-Lab/RuAdapt) для задачи упрощения текста (text simplification).
from datasets import load_dataset
data_files = {'train':"train.csv",'test':"test.csv"}
dataset = load_dataset("r1char9/simplification", data_files=data_files)
train_df = dataset['train'].to_pandas()
test_df = dataset['test'].to_pandas()
wikilarge-text-simplificationSimpleMath
🧮 SimpleMath 100K
SimpleMath 100K is a high-quality synthetic dataset of 100,000 basic arithmetic problems — no noise, no tricks, just clean and accurate math.
✅ Purpose
This was made for small AI models — not to struggle with complex math, but to get simple math right every time.
📦 Contents
75,000 numeric problems, evenly split:
18,750 addition (456 + 789 =)
18,750 subtraction (900 - 345 =)
18,750 multiplication (12 x 15 =)
18,750 division (144 / 12 =)… See the full description on the dataset page: https://huggingface.co/datasets/ProCreations/SimpleMath.SQuAD_v1.1_Du_et_al_2017_formattedThe Du. et. al. 2017 paper provides the splits fo the SQuAD v1.1 dataset
in the json format. However, they are formatted differently than the original SQuAD dataset as posted
on huggingface.
So for ease of use in your own code, I'm providing a formatted version of the data with splits that were used in that paper.
The data_preprocessing.py script is also provided for convenience.
NOTE: The 'answers' column is stored as a string. This is because I exported the dataframe as .csv. So the… See the full description on the dataset page: https://huggingface.co/datasets/simpleParadox/SQuAD_v1.1_Du_et_al_2017_formatted.chatgpt-dutch-simplification
Dataset Card for ChatGPT Dutch Simplification
Dataset Summary
Created in light of a master thesis by Charlotte Van de Velde as part of the Master of Science in Artificial Intelligence at KU Leuven.
Charlotte is supervised by Vincent Vandeghinste and Bram Vanroy.
The dataset contains Dutch source sentences and aligned simplified sentences, generated with ChatGPT. All splits combined, the dataset
consists of 1267 entries.
Charlotte used gpt-3.5-turbo with the following… See the full description on the dataset page: https://huggingface.co/datasets/BramVanroy/chatgpt-dutch-simplification.Simplified_Chinese_Multi-Emotion_Dialogue_Dataset
Simplified_Chinese_Multi-Emotion_Dialogue_Dataset
数据说明
本数据是简体中文口语情感分类数据集
翻译自于：Johnson8187/Chinese_Multi-Emotion_Dialogue_Dataset
使用Qwen2.5-32B-instruce模型将其从繁体中文翻译为简体中文
共4159条
情感类别
原始类别
翻译后类别
条数
悲傷語調
伤心
486
憤怒語調
生气
527
關切語調
关心
560
驚奇語調
惊讶
499
開心語調
开心
592
平淡語氣
平静
705
厭惡語調
厌恶
404
audsem-simple
AudSem Dataset without Semantic Descriptors
Accompanying paper: https://arxiv.org/abs/2505.14142.
GitHub repo: https://github.com/gljs/audsemthinker
Dataset Description
Overview
The AudSem-Simple dataset (audsem-simple) is a streamlined version of the AudSem dataset, designed to enhance the reasoning capabilities of Audio-Language Models (ALMs) through structured thinking over sound, but without explicit semantic element breakdowns.
The dataset includes four… See the full description on the dataset page: https://huggingface.co/datasets/gijs/audsem-simple.simple_questionsORCHESTRA-simple-1M
ORCHESTRA-simple-1M
GitHub: nk2028/ORCHESTRA-dataset
中文簡介
ORCHESTRA (cOmpRehensive Classical cHinESe poeTRy dAtaset) 是一個全面的古典中文詩歌的數據集，數據來自搜韻網。本數據集由 nk2028 進行格式轉換並發佈，希望透過公開高品質的古典中文詩歌數據，促進對古典中文詩歌及古典中文自然語言處理的研究。
ORCHESTRA-simple 是 ORCHESTRA 數據集的簡化格式，僅保留 id, title, group_index, type, dynasty, author, content 這 7 個欄位，而去除其他欄位，以簡化使用。
本資料集可用於大型語言模型的訓練。如欲作其他用途，請向數據提供者搜韻網諮詢。
English Introduction
ORCHESTRA (cOmpRehensive Classical cHinESe poeTRy dAtaset) is a comprehensive dataset of classical… See the full description on the dataset page: https://huggingface.co/datasets/Ayaka/ORCHESTRA-simple-1M.SimpleDC
simpledc-dataset
Official huggingface dataset for the SimpleDC (Simple Digestive Cancer) dataset
Please cite as:
@article{rahman2024health,
title={Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement Learning},
author={Rahman, Md Mushfiqur and Irbaz, Mohammad Sabik and North, Kai and Williams, Michelle S and Zampieri, Marcos and Lybarger, Kevin},
journal={arXiv preprint arXiv:2401.15043},
year={2024}
}
Simple-FriendlyMath
Simple-FriendlyMath
Simple-FriendlyMath is a lightweight dataset focused on basic math with a friendly, conversational style.It's designed to help small and medium models learn not just to calculate — but also to explain answers in a natural, helpful way.
Built as a natural follow-up to SimpleMath, this dataset keeps things easy while adding warmth, chat phrasing, and simple explanations.
What's Inside
10,000 examples of addition, subtraction, multiplication, and… See the full description on the dataset page: https://huggingface.co/datasets/ProCreations/Simple-FriendlyMath.SimpleQnA
SimpleQnA Dataset Description
SimpleQnA is a straightforward question-and-answer dataset in Tagalog. It covers many topics and task types, mixing simple and complex instructions. Some instructions are in English to avoid the model learning Tagalog-only responses when given Tagalog prompts.
The dataset includes constraints borrowed from Tulu 3, Muffin, and IFEval for instruction-following diversity. Random sampling is used to enhance variety.
Topics and Task Types… See the full description on the dataset page: https://huggingface.co/datasets/MaAIos/SimpleQnA.simple-image-captionsleipzig_en_simple_wikipedia_2021_sentences_100ksimple-codeSimple code is a dataset consisting of simple code problems, such as converting Celcius to Fahrenheit in Python, adding/dividing numbers in C++, and so on.
If you have any ideas/questions/concerns for the dataset, feel free to open a community discussion :)
-Aclevo
simple-club-523d12
simple-club-523d12
Synthetic products test data: 55 rows in data.csv.
All values are randomly generated fictional examples, not real observations, products, or user activity. Intended only for CSV loading and pipeline tests; not suitable for scientific or business conclusions. Columns are sampled independently and do not model real-world correlations.
Fields
sample_id: random identifier for this generated sample.
row_id: sequential row number starting at 1.… See the full description on the dataset page: https://huggingface.co/datasets/seongjin042/simple-club-523d12.simple_english_wikipedia_p0Only for the researching usage.
The converting process below.
# Setting the env
os.environ['DATASET_URL']='http://sbert.net/datasets/simplewiki-2020-11-01.jsonl.gz'
os.environ['MODEL_NAME']='multi-qa-MiniLM-L6-cos-v1'
# Loading the dataset
import json
import gzip
from sentence_transformers.util import http_get
http_get(os.getenv('DATASET_URL'), os.getenv('DATASET_NAME'))
passages=[]
with gzip.open(os.getenv('DATASET_NAME'), 'rt', encoding='utf8') as fIn:
for line in fIn:… See the full description on the dataset page: https://huggingface.co/datasets/aisuko/simple_english_wikipedia_p0.