datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Math_CoT_Arabic_English_Reasoning
Math CoT Arabic English Dataset
A high-quality, bilingual (English & Arabic) dataset for Chain-of-Thought (COT) reasoning in mathematics and related disciplines, developed by Miscovery AI.
Overview
Math-COT is a unique dataset designed to facilitate and benchmark the development of chain-of-thought reasoning capabilities in language models across mathematical domains. With meticulously crafted examples, explicit reasoning steps, and bilingual support, this dataset offers… See the full description on the dataset page: https://huggingface.co/datasets/miscovery/Math_CoT_Arabic_English_Reasoning.BanglaSleep-CoT
BanglaSleep-CoT
The first Bengali-language sleep health instruction dataset with chain-of-thought reasoning traces.
Built for the Uncharted Data Challenge by Adaption Labs.
Expanded using Adaptive Data by Adaption.
Dataset at a Glance
Why This Dataset Exists
Every major sleep health AI model — Google PH-LLM (Nature Medicine, 2025), PaPaGei (ICLR 2025), WatchSleepNet (CHIL 2025) — was trained exclusively on Western clinical… See the full description on the dataset page: https://huggingface.co/datasets/tasfuuu19/BanglaSleep-CoT.learn2zinc-cot
Learn2Zinc-CoT: Dataset for MiniZinc Generation with CoT Reasoning
Overview
Learn2Zinc-CoT is a supervised fine-tuning dataset for training large language models to translate natural-language optimization problems into MiniZinc code using an explicit chain-of-thought reasoning step. Each example first produces a structured reasoning block (identifying variables, constraints, and the objective) before generating the MiniZinc model.
This dataset is part of the Learn2Zinc… See the full description on the dataset page: https://huggingface.co/datasets/skadio/learn2zinc-cot.PersonalFinance-CoTR-5K
PersonalFinance-CoTR Dataset (v0.1.0)
[Dataset is Under Active Development]
Note This dataset is being iteratively developed. At the current stage of the dataset, V0.1.0 would be a dataset of ~5k datapoints, that are different user-responses.
Overview
A growing dataset of Chain-of-Thought Responses to personal finance queries asked by users on r/PersonalFinance subreddit.
Status: Early development (10 samples → expanding to 54k)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Akhil-Theerthala/PersonalFinance-CoTR-5K.ua-codeforces-cots-open-r1
Dataset Summary
ua-codeforces-cots-open-r1 is a Ukrainian-focused derivative of open-r1/codeforces-cots that:
includes 1550 Python solutions from original dataset generated by DeepSeek-R1;
adds Ukrainian translations of Codeforces task statements, I/O formats, notes, and editorials;
provides Ukrainian translation of original ("high") reasoning obtained with DeepSeek-V3;
adds “low” reasoning in Ukrainian by DeepSeek-R1 based on original reasoning and task statements;
ships… See the full description on the dataset page: https://huggingface.co/datasets/anon-researcher-ua/ua-codeforces-cots-open-r1.pashto-eagle-1k-cot
Pashto-Eagle-1K-CoT Dataset
Overview
Pashto-Eagle-1K-CoT is a high-fidelity reasoning dataset tailored for the Pashto language. It consists of 1,024 samples featuring complex logic, mathematical reasoning, and step-by-step problem-solving. This dataset is a translated and refined version of the brendan-gho/qwen3b_paraphrased_eagle_cot.
This repository is part of the iPashto.ai initiative to build a robust open-source ecosystem for Pashto Artificial Intelligence, focusing… See the full description on the dataset page: https://huggingface.co/datasets/nassimjp/pashto-eagle-1k-cot.flanv2_cot_dedepulicated
FLAN v2 Cot Deduplicated Dataset
Data Preprocessing
Remove instructions with less than 100 tokens in 'targets'.
Dedepulicate Dataset using cosine similarity with a threshold of 0.95.
Code
Github repo : https://github.com/AJlearner46/Deduplicate-flanv2-finetune-LLaMa3-
Acknowledgments
The original dataset is provided by SirNeural/flan_v2.
Tokenizer used: bert-base-uncased from Hugging Face.
pashto-otter-cot
Pashto-Otter-CoT Dataset
Overview
Pashto-Otter-CoT is a first-of-its-kind dataset specifically designed to bring Chain-of-Thought (CoT) Reasoning capabilities to Pashto language models. This dataset is a translated and curated version of a subset of the brendan-gho/gemma4b_paraphrased_otter_cot.
This project is part of the iPashto.ai initiative, led by Nassim الله (nassimjp), aimed at creating high-quality linguistic resources for the Pashto language.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/nassimjp/pashto-otter-cot.pashto-dragon-1k-cot
Pashto-Dragon-1K-CoT Dataset
Overview
Pashto-Dragon-1K-CoT is a specialized reasoning dataset containing 1,000+ samples, meticulously translated into Pashto to facilitate the development of advanced Chain-of-Thought (CoT) capabilities in Pashto LLMs. This dataset is a high-quality derivative of the brendan-gho/qwen3b_paraphrased_dragon_cot.
This repository is a core component of the iPashto.ai mission to move beyond simple web-scraping and focus on "Verified Reasoning… See the full description on the dataset page: https://huggingface.co/datasets/nassimjp/pashto-dragon-1k-cot.pashto-qwen-1k-cot
Pashto-Qwen-1K-CoT Dataset
Overview
Pashto-Qwen-1K-CoT is a high-quality reasoning dataset consisting of 1,024 samples, specifically curated to enhance the Chain-of-Thought (CoT) capabilities of Pashto language models. This dataset is a translated version of a subset from brendan-gho/qwen3b_paraphrased_cat_cot.
By focusing on "Reasoning" rather than just "Information," this dataset helps models like Baran and Roshan develop logical thinking paths in the Pashto language.… See the full description on the dataset page: https://huggingface.co/datasets/nassimjp/pashto-qwen-1k-cot.schema_cot_reasoning
⊙ Prompt Programs for Agentic Reasoning
Programmable task-dependent COTs for agentic reasoning.
A 100-row seed dataset for programmable cognition.
Each row defines:
Prompt template + input binding + explanation + task-dependent reasoning program
Pipeline:
intake → binding → procedure → output
Schema
Column
Meaning
ID
Stable row ID
Name
Task name
Prompt
Prompt template using {{VARIABLE}}
Expression
Input binding using $.path
Explanation
Binding… See the full description on the dataset page: https://huggingface.co/datasets/bitwikiorg/schema_cot_reasoning.simple_algebra_CoT_1mDataset: Chain-of-Thought ArithmeticSize: 1M examples
Categories
Simple (50%)
Range: 0 ≤ a, b ≤ 1,000
Ops: Addition (a + b), Subtraction (a - b)
Hard (50%)
Range: -100,000 ≤ a, b ≤ 100,000
Ops: Addition (a + -b), Subtraction (-a - b)
(Data generated via algorithmic step-by-step breakdown.)