datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
aec-challenge-16k
Microsoft AEC Challenge 16kHz
Microsoft AEC Challenge dataset
converted from 16kHz WAV to FLAC (lossless compression) and packed into tar shards.
Source: the datasets/ directory of the microsoft/AEC-Challenge Git LFS repo.
Covers all challenge years (2021, ICASSP 2022, ICASSP 2023).
Structure
Real recordings
Paired loopback (far-end reference) and microphone recordings from real devices.
real/ — 37,578 files, single playback real recordings
real_doubled/ — 10… See the full description on the dataset page: https://huggingface.co/datasets/richiejp/aec-challenge-16k.aec-bench
AEC-Bench: A Multimodal Dataset for Architecture, Engineering, and Construction
Section
What it covers
Overview
What the dataset contains
Task taxonomy
Scopes, task families, instance counts
Accessing the dataset
manifest.jsonl, prefetching files from URLs
License
Apache 2.0
Citation
BibTeX
Overview
AEC-Bench is a multimodal dataset of real-world Architecture, Engineering, and Construction (AEC) documents — construction drawings, floor… See the full description on the dataset page: https://huggingface.co/datasets/nomic-ai/aec-bench.aec-challenge-16k
Microsoft AEC Challenge 16kHz
Microsoft AEC Challenge dataset
converted from 16kHz WAV to FLAC (lossless compression) and packed into tar shards.
Source: the datasets/ directory of the microsoft/AEC-Challenge Git LFS repo.
Covers all challenge years (2021, ICASSP 2022, ICASSP 2023).
Structure
Real recordings
Paired loopback (far-end reference) and microphone recordings from real devices.
real/ — 37,578 files, single playback real recordings… See the full description on the dataset page: https://huggingface.co/datasets/Robby-dev/aec-challenge-16k.aec_v1
Dataset Card for AEC
Dataset Description
The Agricultural Extension Corpus 1.1 is a compilation of 1655 official agricultural Extension documents (e.g., fact sheets, digital books) concerning water-related and sustainable agricultural practices research.
Homepage: https://huggingface.co/datasets/msu-ceco/aec_v1
Paper: AgXQA: A benchmark for advanced Agricultural Extension question answering
GitHub: agxqa_benchmark_v1
Curated by: DSI Lab
Point of Contact: pouyan@msu.edu… See the full description on the dataset page: https://huggingface.co/datasets/msu-ceco/aec_v1.aec-challenge-synthetic-mini
AEC-Challenge synthetic mini (200 examples)
First 200 examples (shard 0, dataset order) of the Microsoft AEC-Challenge
synthetic set (https://github.com/microsoft/AEC-Challenge/tree/main/datasets/synthetic,
Sridhar et al., ICASSP 2021, arXiv:2009.04972), exported from the
PandaLT/microsoft-AEC-dataset parquet mirror as 16 kHz 16-bit mono WAV:
fileid_<id>_mic.wav near-end microphone signal (near-end speech + echo, optionally noise)
fileid_<id>_lpb.wav far-end / loopback… See the full description on the dataset page: https://huggingface.co/datasets/FluidInference/aec-challenge-synthetic-mini.aec-challenge-48k
AEC Challenge 48 kHz Community Mirror
This dataset is a community mirror of the 48 kHz synthetic_fullband data from the
2022 AEC Challenge. The original upstream download is no longer available.
The recovered archive contains three synchronized audio tracks:
target: near-end target speech
mic: microphone mixture
farend: far-end reference audio
This is not an official Microsoft repository. The mirror preserves the recovered
archive as-is and does not add generated training… See the full description on the dataset page: https://huggingface.co/datasets/haor/aec-challenge-48k.release-model-rollouts
AEC-Bench Release Model Rollouts
This dataset contains model rollouts from the first AEC-Bench release evaluation suite: model answers, trial metadata, execution traces, and task taxonomy for generated Architecture, Engineering, and Construction benchmark tasks.
AEC-Bench is a Python platform for creating, running, and evaluating AI agents on realistic AEC tasks. The benchmark is described in AEC-Bench: A Multimodal Benchmark for Agentic Systems in Architecture, Engineering, and… See the full description on the dataset page: https://huggingface.co/datasets/aec-bench/release-model-rollouts.AECBench
🏗️ AECBench
🇺🇸 English | 🇨🇳 中文说明
Project Introduction
AECBench is an open-source large language model Architecture, Engineering & Construction (AEC) domain evaluation benchmark jointly released by East China Architectural Design & Research Institute Co., Ltd. (ECADI) of China Construction Group and Tongji University. This dataset aims to systematically evaluate large language models' (LLMs) knowledge mastery, understanding, reasoning… See the full description on the dataset page: https://huggingface.co/datasets/jackluoluo/AECBench.AEConvs
Dataset Card for AEConvs
AEConvs (Arabic Empathetic Conversations) is a genuine Arabic conversational dataset that features more than
4K open-domain dyadic empathetic conversations. Each conversation in the dataset was
conducted between two humans, resulting in authentic and natural conversations. The dataset is written in Modern Sandard Arabic (MSA). which is the formal and standardized form of the Arabic language used
across the Arabic-speaking world.
The AEConvs dataset is… See the full description on the dataset page: https://huggingface.co/datasets/afnankhth/AEConvs.aec-rag-dataset
Lumen-Models: AEC-RAG Dataset
Lumen-Models is the premier conversational dataset designed to fine-tune LLMs and empower RAG (Retrieval-Augmented Generation) systems within the Architecture, Engineering, and Construction (AEC) sector.
This dataset features high-fidelity technical dialogues between a BIM Auditor and a GPT Expert, focused on solving real-world challenges regarding regulatory compliance, complex construction codes, and professional industry standards.
Premium… See the full description on the dataset page: https://huggingface.co/datasets/lumen-models/aec-rag-dataset.scierc_aeco
Dataset Card for SciERC AECO dataset
Dataset Summary
The SciERC AECO dataset is an English-language dataset containing 1016 sentences from research papers in the AECO domain, annotated for scientific entities and relations based on the SciERC annotation schema.
Supported Tasks and Leaderboards
'NER': the dataset can be used to train a model to detect scientific entities according to the SciERC annotation schema
'Relation extraction': the dataset can be used… See the full description on the dataset page: https://huggingface.co/datasets/zavavan/scierc_aeco.Microsoft-AEC-Silero-VADAEC_V3AEC_V5test-2fd961bc-aec9-4cec-8f2a-9e5ce676127aAEC_V2AEC_V1africa-mauritius-pension-for-invalid-orphan-retirement-severelyhandicapped-aec4b0e3
Pension for Invalid Orphan Retirement Severelyhandicapped | Africa (MDPA)
75 rows - 1 Africa country/area - 1990-2014 - 3 indicators - Engineered by Electric Sheep Africa
TL;DR
This dataset contains 75 rows from MDPA, covering Pension for Invalid Orphan Retirement Severelyhandicapped. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.
What This Dataset Measures… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-pension-for-invalid-orphan-retirement-severelyhandicapped-aec4b0e3.AEC_V6AEC-VA-details-spec-datasetafrica-niger-niger-most-likely-fews-net-acutely-food-insecure-populatio-aece7e8a
Niger Most Likely FEWS NET Acutely Food Insecure Population Estimates Data | Africa (Niger official open data)
864 rows - 1 Africa country - 2023-2026 - Repackaged by Electric Sheep Africa
TL;DR
This dataset packages one official XLSX resource from Niger as
ML-ready Parquet. The source file is the provenance boundary; all usable
indicators or tabular columns from the resource stay together in this repo.
About the source
Source: Niger Most… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-niger-niger-most-likely-fews-net-acutely-food-insecure-populatio-aece7e8a.AEC_V4bild-ecce347c-b8e4-4e99-aec3-e30a53023852aecf0e30fe6db3a5e251b9dca3743d4microsoft-AEC-datasetgemma4-aec-datasettest_import_dataset_from_hub_with_classlabel_45ae474f-e10d-4001-aec2-a5806082d783test_import_dataset_from_hub_with_classlabel_aece79d8-470d-4205-a439-614f44206064test_import_dataset_from_hub_with_classlabel_09399e65-043f-4d73-9727-aececb6fee77microsoft-AEC-vad-dataset