datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
AIForge-1K-Architecture
AIForge-05-Architecture
Architecture Dataset for AI and Programming Tasks
Overview
AIForge-05-Architecture is a curated English dataset designed for AI systems working on architecture tasks in software engineering and programming.
Contents
data.jsonl
data.json
metadata.json
Use Cases
AI agent training
Supervised fine-tuning
Evaluation and benchmarking
Software engineering research
Example Record
{
"id":… See the full description on the dataset page: https://huggingface.co/datasets/jumplander/AIForge-1K-Architecture.azure-architecture-vqa
Azure Architecture Visual Question Answering Dataset
A comprehensive visual question answering (VQA) dataset for fine-tuning vision language models to become Azure Cloud Solution Architects. Created from the Azure Architecture Center.
Dataset Description
This dataset contains Q&A pairs paired with Azure architecture diagrams, designed for fine-tuning vision language models (like Qwen 3.5 VL) to understand and reason about cloud architecture patterns.
Source… See the full description on the dataset page: https://huggingface.co/datasets/thegovind/azure-architecture-vqa.architecture-construction-fr
Architecture Construction FR
Architecture Construction FR is a French synthetic reasoning dataset for architecture, construction, project documentation, construction-site workflows, BIM, environmental design and building handover in France.
The current release is v1.1.
It should be considered a first experimental release rather than a definitive domain dataset.
It extends an internal v1 pilot with a balanced taxonomy around decision support, documentary checks, chantier/DET/OPR… See the full description on the dataset page: https://huggingface.co/datasets/El1iasss/architecture-construction-fr.architecture_faqsJapanese construction themes FAQs scraped from https://www.city.yokohama.lg.jp/business/bunyabetsu/kenchiku/annai/faq/qa.html.
Downloaded using the following code:
import requests
from lxml import html
import pandas as pd
from datasets import Dataset
hrefs = [
"/business/bunyabetsu/kenchiku/annai/faq/ji-annnai.html",
"/business/bunyabetsu/kenchiku/tetsuduki/kakunin/qa-kakunin.html",
"/business/bunyabetsu/kenchiku/tetsuduki/teikihoukoku/seido/01.html"… See the full description on the dataset page: https://huggingface.co/datasets/lightblue/architecture_faqs.