datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SimpleStories
📘📕 SimpleStories 📙📗
SimpleStories is a dataset of >2 million model-generated short stories. It was made to train small, interpretable language models on it. The generation process is open-source: To see how the dataset was generated, or to generate some stories yourself, head over to this repository.
If you'd like to commission other languages or story formats, feel free to send mail.
When using SimpleStories in your work, please cite the SimpleStories paper:… See the full description on the dataset page: https://huggingface.co/datasets/SimpleStories/SimpleStories.sim-posttrain
HUMANUAL Posttraining Data
Posttraining data for user simulation, derived from the train splits of the
HUMANUAL benchmark datasets.
Datasets
HUMANUAL (posttraining)
Config
Rows
Description
news
48,618
News article comment responses
politics
45,429
Political discussion responses
opinion
37,791
Reddit AITA / opinion thread responses
book
34,170
Book review responses
chat
23,141
Casual chat responses
email
6,377
Email reply responses… See the full description on the dataset page: https://huggingface.co/datasets/Xuhui/sim-posttrain.multilingual-textarena-SimpleTak-v0-train-v2
TextArena Language Trajectories
This dataset contains language-conditioned TextArena trajectory data.
Each dataset configuration corresponds to a different model, experiment group, or source folder.
Available configurations:
gemma4-e4b-it
qwen3-4b
ministral3-3b-instruct
Usage
Install the datasets library:
pip install datasets
Load a specific configuration:
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/The-CoLab/multilingual-textarena-SimpleTak-v0-train-v2.simpsons_infoThe information of all episodes of the cartoon show "The Simpsons" from wikipedia. Some (mainly in recent 32, 33, 34 seasons) plot missing.
SimpleStories-JA
📘📕 SimpleStories 📙📗
このデータセットは、gpt-4o-miniによって生成された短編小説で出来ているデータセットです。生成方法や、自分で物語を生成する方法については、こちらのリポジトリをご覧ください。
他の言語や物語形式の制作を希望される場合は、メールにてお問い合わせください。
SimpleStoriesは、EldenとLiによるTinyStoriesの改良版です。
特徴
物語の注釈情報（theme、topic、styleなど）
多様性の高さ
2024年のモデルによって生成
NLPのデータが用意しているためフィルタリングしやすい
以下の言語版が利用可能：
英語
日本語
他にも追加予定
This dataset is a collection of short stories generated by gpt-4o-mini (+ other models, soon). To see how this dataset was generated, or to generate some stories… See the full description on the dataset page: https://huggingface.co/datasets/SimpleStories/SimpleStories-JA.multilingual-textarena-SimpleTak-v0-train
TextArena Language Trajectories
This dataset contains language-conditioned TextArena trajectory data.
Each dataset configuration corresponds to a different model, experiment group, or source folder.
Available configurations:
gemma4-e4b-it
qwen3-4b
ministral3-3b-instruct
Usage
Install the datasets library:
pip install datasets
Load a specific configuration:
from datasets import load_dataset
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/The-CoLab/multilingual-textarena-SimpleTak-v0-train.sakhi
Sakhi: A Community-Validated Multilingual Maternal-Health Benchmark
Sakhi is a benchmark for evaluating large language models on maternal and reproductive-health questions in three languages spoken in low-resource settings: English, Hindi, and Marathi. It was built around a deployed WhatsApp-based maternal-health chatbot reaching rural mothers in Hindi- and Marathi-speaking districts of India, with a three-channel review pipeline: practising Indian doctors, Accredited Social Health… See the full description on the dataset page: https://huggingface.co/datasets/SimPPL/sakhi.age-specific-text-simplification
Age-Specific Text Simplification Dataset
Dataset Description
This dataset contains complex texts simplified into age-appropriate versions for children aged 3, 4, and 5 years old. Each original text has been professionally adapted to match the cognitive development, vocabulary, and comprehension abilities of each specific age group.
Dataset Summary
Total Examples: 17,177
Training Split: 15,459 examples
Validation Split: 1,718 examples
Languages:… See the full description on the dataset page: https://huggingface.co/datasets/hasankursun/age-specific-text-simplification.SimpleStories
📘📕 SimpleStories 📙📗
SimpleStories is a dataset of >2 million model-generated short stories. It was made to train small, interpretable language models on it. The generation process is open-source: To see how the dataset was generated, or to generate some stories yourself, head over to this repository.
If you'd like to commission other languages or story formats, feel free to send mail.
When using SimpleStories in your work, please cite the SimpleStories paper:… See the full description on the dataset page: https://huggingface.co/datasets/duoduoyeah/SimpleStories.iterative-dpo-data-for-SimPO-iter2
iterative-dpo-data-for-SimPO-iter2
概要
合成instructionデータであるAratako/Magpie-Tanuki-Instruction-Selected-Evolved-26.5kを元に以下のような手順で作成した日本語Preferenceデータセットです。
開発途中のモデルであるAratako/Llama-Gemma-2-27b-CPO_SimPO-iter1を用いて、temperature=1で回答を5回生成
5個の回答それぞれに対して、Qwen/Qwen2.5-72B-Instruct-GPTQ-Int8を用いて0～5点のスコア付けを実施
1つのinstructionに対する5個の回答について、最もスコアが高いものをchosenに、低いものをrejectedに配置
全て同じスコアの場合や、最も良いスコアが2点以下の場合は除外
ライセンス
本データセットは回答の作成に利用したモデルの関係で以下のライセンスの影響を受けます。
META LLAMA 3.1… See the full description on the dataset page: https://huggingface.co/datasets/Aratako/iterative-dpo-data-for-SimPO-iter2.ORCHESTRA-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.Simple-agent-traces
📱 Simple Agent Traces – Tiny Tool‑Calling Conversations for Small Models
Simple Agent Traces is a compact, hand‑picked dataset of 605 real‑world tool‑calling conversations, each carefully truncated to ≤8,192 tokens (using the SmolLM2‑360M tokenizer).It is purpose‑built for training and fine‑tuning tiny language models (≤500M) that must run on‑device – smartphones, edge devices, or any environment with strict memory and latency constraints.
🧹 No chain‑of‑thought, no fluff.Every… See the full description on the dataset page: https://huggingface.co/datasets/LiteMind/Simple-agent-traces.countdown-qwen3-0.6b
Countdown Qwen3-0.6B Pass@10 Buckets
Countdown arithmetic problems filtered by observed local Qwen/Qwen3-0.6B success rate over 10 rollouts per problem.
Each problem asks for an arithmetic expression that reaches a target using each listed source number at most once. The final answer should be inside \boxed{...}. Canonical solutions are provided, but any verifier-valid expression is accepted.
Subsets
subset
source bucket
count
observed successes out of 10… See the full description on the dataset page: https://huggingface.co/datasets/simpissa/countdown-qwen3-0.6b.controlled_text_simplyControlled text simplification, targetting at different audiences. A dataset for a group project.
bayan-simplification-corpus
Synthetic simplification data card — v0-provisional
Status: provisional. This export applies the readability-lead quarantine (see below) but has
not yet incorporated the morning relabel this thread's expert consultation called for (the four
strata: lead<=0, near-margin d_logit-TAU, tier A/B boundary, spike-in retest). Numbers here are
the honest current state, not a final release. Re-run scripts/dataset_split.py once the relabel
lands; the split mechanism itself does not need to… See the full description on the dataset page: https://huggingface.co/datasets/Congi-libya/bayan-simplification-corpus.