datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
motion-smd-data
Motion-SMD Data
Data release for "Encoder-Free Human Motion Understanding via Structured Motion Descriptions".
🌐 Project page: https://yaozhang182.github.io/motion-smd/
💻 Code: https://github.com/yaozhang182/motion-smd
🤗 LoRA adapters: https://huggingface.co/zyyy12138/motion-smd-lora
📄 Paper (arXiv): https://arxiv.org/abs/2604.21668
What's here
Four subdirectories, each with its own README.md describing files, provenance, and license:
Subdir
Contents
Our… See the full description on the dataset page: https://huggingface.co/datasets/zyyy12138/motion-smd-data.motif-qa
MotifQA
Dataset Summary
MotifQA is a synthetic graph question-answering benchmark focused on detecting graph motifs inside small random graphs.
Each example pairs a textual prompt with an answer sentence, a list of nodes highlighted as the motif (when present), and an explicit graph description(nodes and edges).
In this QA dataset, all graphs are homogenous and undirected.
Subsets cover both yes/no motif detection, motif-type classification (house vs 5-cycle), and… See the full description on the dataset page: https://huggingface.co/datasets/naos-ku/motif-qa.STRIDE-QA-Dataset-Mini
STRIDE-QA-Dataset-Mini
STRIDE-QA is a large-scale visual question answering (VQA) dataset for physically grounded spatiotemporal reasoning in autonomous driving. Constructed from 100 hours of multi-sensor driving data in Tokyo, it offers 16 M QA pairs over 270 K frames with dense annotations including 3D bounding boxes, segmentation masks, and multi-object tracks.
⚠️ Note: STRIDE-QA-Dataset-Mini is provided as a preliminary version and does not fully match the format of the… See the full description on the dataset page: https://huggingface.co/datasets/turing-motors/STRIDE-QA-Dataset-Mini.LLaVA-v1.5-Instruct-620K-JA
Dataset Details
Dataset Type:Japanese LLaVA v1.5 Instruct 620K is a localized version of part of the original LLaVA v1.5 Visual Instruct 655K dataset. This version is translated into Japanese using DeepL API and is aimed at serving similar purposes in the context of Japanese language.
Resources for More Information:For information on the original dataset: LLaVA
License:Attribution-NonCommercial 4.0 International (CC BY-NC-4.0)The dataset should abide by the policy of OpenAI: OpenAI… See the full description on the dataset page: https://huggingface.co/datasets/turing-motors/LLaVA-v1.5-Instruct-620K-JA.EarthScience-Text-LLM-20K-90-10
EarthScience-Text-LLM-20K-90-10
This is a pure-text Earth-science corpus unified from three non-overlapping upstream datasets:
Ekimetrics/climateqa-ipcc-ipbes-reports-1.0: climate and IPCC/IPBES report chunks.
GeoGPT-Research-Project/GeoGPT-CoT-QA: geoscience question-answer reasoning.
gremlin97/RemoteSensingCorpus: remote-sensing and geospatial machine-learning text.
Files and Split
The previous preprocessing outputs were merged into a 23,098-record pool and… See the full description on the dataset page: https://huggingface.co/datasets/moTcream/EarthScience-Text-LLM-20K-90-10.LLaVA-Pretrain-JA
Dataset Details
Dataset Type:Japanese LLaVA Pretrain is a localized version of the original LLaVA Pretrain dataset. This version is translated into Japanese using DeepL API and is aimed at serving similar purposes in the context of Japanese language.
Resources for More Information:For information on the original dataset: LLaVA
License:License: Must comply with license of CC-3M, BLIP (if you use their synthetic caption).
CC-3M The dataset may be freely used for any purpose, although… See the full description on the dataset page: https://huggingface.co/datasets/turing-motors/LLaVA-Pretrain-JA.AI-Agent-Generating-Tool-Debugging-Prompt-Library
Dataset Card for "AI Agent Generating Tool & Debugging Prompt Library" 🤖⚙️
Dataset Details 📚
Dataset Name: AI Agent Generating Tool & Debugging Prompt Library
Dataset Description:This dataset includes a collection of prompts focused on building and debugging AI-driven tools, including creating self-improving AI agents and debugging prompts for Python projects. The dataset is designed for use in fine-tuning models related to code generation, debugging, and software… See the full description on the dataset page: https://huggingface.co/datasets/Chemically-motivated/AI-Agent-Generating-Tool-Debugging-Prompt-Library.MoT-Code-350K
🏠 MoTCode-Data
• 🤗 Data • 🤗 Model • 🐱 Code • 📃 Paper
Dataset Structure
from datasets import load_dataset
load_dataset("JingyaoLi/MoT-Code-350K")
DatasetDict({
train: Dataset({
features: ['instruction', 'output'],
num_rows: 312645
})
})
Modular-of-thought Data Creation
We provide an example python file to evolution a MoT dataset. Run the following command:
python src/generate_MoT_dataset.py \
--data_path $data_path \… See the full description on the dataset page: https://huggingface.co/datasets/JingyaoLi/MoT-Code-350K.MotiveBench
MotiveBench
This is the official repository for our ACL 2025 paper "MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models?"
Dataset Description
MotiveBench is a benchmark for evaluating the human-like motivational and behavioral reasoning capabilities of large language models (LLMs). It consists of 200 diverse profiles and 600 reasoning tasks, covering multiple levels of motivation based on Maslow's Hierarchy of Needs. The dataset… See the full description on the dataset page: https://huggingface.co/datasets/chicosirius/MotiveBench.mot-dang-chiang-mai-chiang-rai
มดแดง Mot Dang — Chiang Mai & Chiang Rai city directory
88,161 places in and around Chiang Mai (62,772) and Chiang Rai (25,389),
in Thai and English, with coordinates, categories, opening hours, and the
channels a place actually answers on — phone, LINE, Facebook, a website that
still resolves.
The name is มดแดง, mot daeng, the red ant: the thing that knows every soi
because it has walked all of them. That is the ambition. The directory exists
because mainstream mapping is thin… See the full description on the dataset page: https://huggingface.co/datasets/NaNoBotCo/mot-dang-chiang-mai-chiang-rai.motion_planning_for_automated_drivingThis question-answer dataset is extracted from paper A Survey on Hybrid Motion Planning Methods for Automated Driving Systems written by MReza Alipour Sormoli, Konstantinos Koufos, Mehrdad Dianati, Senior Member, IEEE, and Roger Woodman. Deepseek R1 is used as the extraction tool.
LLaVA-Instruct-150K-JA
Dataset Details
Dataset Type:Japanese LLaVA Instruct 150K is a localized version of the original LLaVA Visual Instruct 150K dataset. This version is translated into Japanese using DeepL API and is aimed at serving similar purposes in the context of Japanese language.
Resources for More Information:For information on the original dataset: LLaVA Visual Instruct 150K
License:Attribution-NonCommercial 4.0 International (CC BY-NC-4.0)The dataset should abide by the policy of OpenAI:… See the full description on the dataset page: https://huggingface.co/datasets/turing-motors/LLaVA-Instruct-150K-JA.