datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ifc-bench
IFC-Bench
A benchmark dataset for evaluating BIM (Building Information Modeling) comprehension and reasoning capabilities in AI systems. Provides curated IFC models with question-answer pairs across 4 complexity categories for testing BIM-related AI implementations.
Dataset snapshot:
question
ground_truth
ifc_model
project
category
0
What modelling program and IFC standard were used to create this model?
The model was created using...
arc
4351
1
1
What are the… See the full description on the dataset page: https://huggingface.co/datasets/sylvainHellin/ifc-bench.general-reasoning-ift-pairs
Reasoning-IFT Pairs (General Domain)
This dataset provides the largest set of IFT and Reasoning answers pairs for a set of general domain queries (cf: math-domain).It is based on the Infinity-Instruct dataset, an extensive and high-quality collection of instruction fine-tuning data.
We curated 900k queries from the 7M_core subset of Infinity-Instruct, which covers multiple domains including general knowledge, commonsense Q&A, coding, and math.For each query… See the full description on the dataset page: https://huggingface.co/datasets/Scale-or-Reason/general-reasoning-ift-pairs.general-reasoning-ift-pairs
Reasoning-IFT Pairs (General Domain)
This dataset provides the largest set of IFT and Reasoning answers pairs for a set of general domain queries (cf: math-domain).It is based on the Infinity-Instruct dataset, an extensive and high-quality collection of instruction fine-tuning data.
We curated 900k queries from the 7M_core subset of Infinity-Instruct, which covers multiple domains including general knowledge, commonsense Q&A, coding, and math.For each query, we… See the full description on the dataset page: https://huggingface.co/datasets/Sidsidney/general-reasoning-ift-pairs.procbench
Dataset Card for ProcBench
Dataset Overview
Dataset Description
ProcBench is a benchmark designed to evaluate the multi-step reasoning abilities of large language models (LLMs). It focuses on instruction followability, requiring models to solve problems by following explicit, step-by-step procedures. The tasks included in this dataset do not require complex implicit knowledge but emphasize strict adherence to provided instructions. The dataset evaluates model… See the full description on the dataset page: https://huggingface.co/datasets/ifujisawa/procbench.math-reasoning-ift-pairs
Reasoning-IFT Pairs (Math Domain)
Paper | Project Page
This dataset provides the largest set of IFT and Reasoning answers pairs for a set of math queries (cf: general-domain).
It is based on the Llama-Nemotron-Post-Training dataset, an extensive and high-quality collection of math instruction fine-tuning data.
We curated 150k queries from the math subset of Llama-Nemotron-Post-Training, which covers multiple domains of math questions.For each query, we used… See the full description on the dataset page: https://huggingface.co/datasets/Scale-or-Reason/math-reasoning-ift-pairs.ifc-bench
IFC-Bench
A benchmark dataset for evaluating BIM (Building Information Modeling) comprehension and reasoning capabilities in AI systems. Provides curated IFC models with question-answer pairs across 4 complexity categories for testing BIM-related AI implementations.
Dataset snapshot:
question
ground_truth
ifc_model
project
category
0
What modelling program and IFC standard were used to create this model?
The model was created using...
arc
4351
1
1
What are the… See the full description on the dataset page: https://huggingface.co/datasets/SiloLink/ifc-bench.ifc-bench
IFC-Bench
A benchmark dataset for evaluating BIM (Building Information Modeling) comprehension and reasoning capabilities in AI systems. Provides curated IFC models with question-answer pairs across 4 complexity categories for testing BIM-related AI implementations.
Dataset snapshot:
question
ground_truth
ifc_model
project
category
0
What modelling program and IFC standard were used to create this model?
The model was created using...
arc
4351
1
1
What are the… See the full description on the dataset page: https://huggingface.co/datasets/quenfly/ifc-bench.ifc-bim-qa-dataset
IFC BIM Question-Answering Dataset
A comprehensive question-answering dataset for Building Information Modeling (BIM) and Industry Foundation Classes (IFC) domain knowledge.
Dataset Summary
This dataset contains 13,485 question-answer pairs covering comprehensive BIM domain knowledge:
IFC Schema Knowledge: Entities, constraints, functions, and global rules
IFC Documentation: Specifications, concepts, geometry, and processes
Professional Certification: BIM practices… See the full description on the dataset page: https://huggingface.co/datasets/Dietmar2020/ifc-bim-qa-dataset.IFEval_es
Dataset Card for IFEval_es
IFEval_es is a prompt dataset in Spanish, professionally translated from the main version of the IFEval dataset in English.
Dataset Details
Dataset Description
IFEval_es (Instruction-Following Eval benchmark - Spanish) is designed to evaluating chat or instruction fine-tuned language models. The dataset comprises 541 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times"… See the full description on the dataset page: https://huggingface.co/datasets/BSC-LT/IFEval_es.hacker-news
Hacker News - Complete Archive
Every Hacker News item since 2006, live-updated every 5 minutes
What is it?
This dataset contains the complete Hacker News archive: every story, comment, Ask HN, Show HN, job posting, and poll ever submitted to the site. Hacker News is one of the longest-running and most influential technology communities on the internet, operated by Y Combinator since 2007. It has become the de facto gathering place for founders, engineers, researchers… See the full description on the dataset page: https://huggingface.co/datasets/IFthisisrealitynbds/hacker-news.actuarial-fm-p-ifm-dataset
Actuarial FM + P + IFM Dataset v0.0.7
Dataset Description
Comprehensive training dataset for actuarial AI covering three SOA exams.
Dataset Summary
Total Examples: 18,794
Exam FM: ~18,000 examples
Exam P: 743 examples
Exam IFM: 37 examples
Format: JSONL with instruction-response pairs
Topics Covered
Financial Mathematics (FM)
Time value of money
Annuities and perpetuities
Bonds and interest theory
Amortization
Probability (P)… See the full description on the dataset page: https://huggingface.co/datasets/MorbidCorp/actuarial-fm-p-ifm-dataset.IFEval_ca
Dataset Card for IFEval_ca
IFEval_ca is a prompt dataset in Catalan, professionally translated from the main version of the IFEval dataset in English.
Dataset Details
Dataset Description
IFEval_ca (Instruction-Following Eval benchmark - Catalan) is designed to evaluating chat or instruction fine-tuned language models. The dataset comprises 541 "verifiable instructions" such as "write in more than 400 words" and "mention the keyword of AI at least 3 times"… See the full description on the dataset page: https://huggingface.co/datasets/projecte-aina/IFEval_ca.IFTactuarial-fm-p-ifm-ultimate-dataset
Ultimate Actuarial FM/P/IFM Dataset v0.0.9
Dataset Description
The ultimate training dataset for actuarial AI models, containing 1,708 meticulously crafted examples targeting 95%+ accuracy on professional actuarial exams.
Dataset Statistics
Total Examples: 1,708
Train: 1,366 (80%)
Validation: 170 (10%)
Test: 172 (10%)
Distribution by Exam
Exam
Examples
Percentage
IFM
884
51.8%
P
570
33.4%
FM
254
14.9%
Key Features… See the full description on the dataset page: https://huggingface.co/datasets/MorbidCorp/actuarial-fm-p-ifm-ultimate-dataset.ifc-bim-alpaca-124k
IFC BIM Alpaca Dataset
Dataset Description
This dataset contains 124974 high-quality instruction-following examples for IFC (Industry Foundation Classes) and BIM (Building Information Modeling) in the Alpaca format. It covers IFC 4.3.x-devel standard entities, properties, relationships, and best practices.
Dataset Summary
Total Examples: 124974
Train Set: 112476 examples
Validation Set: 12498 examples
Language: English
Format: Alpaca (instruction, input… See the full description on the dataset page: https://huggingface.co/datasets/Dietmar2020/ifc-bim-alpaca-124k.data-kit-sub-iwslt2025-if-long-constraint
Data for KIT’s Instruction Following Submission for IWSLT 2025
This repo contains the data used to train our model for IWSLT 2025's Instruction-Following (IF) Speech Processing track.
IWSLT 2025's Instruction-Following (IF) Speech Processing track in the scientific domain aims to benchmark foundation models that can follow natural
language instructions—an ability well-established in textbased LLMs but still emerging in speech-based counterparts. Our approach employs an end-to-end… See the full description on the dataset page: https://huggingface.co/datasets/maikezu/data-kit-sub-iwslt2025-if-long-constraint.k-ifrs-qa-dataset
K-IFRS QA Dataset
한국채택국제회계기준(K-IFRS) 기반의 오픈소스 QA 데이터셋입니다.LLM 파인튜닝(SFT), RAG 시스템 구축, 회계 도메인 벤치마크 평가 등 다양한 목적에 활용할 수 있도록 설계되었습니다.
기준 연도: 본 데이터셋은 2026년 5월 24일자 K-IFRS 기준으로 작성되었습니다.회계기준은 지속적으로 개정되므로, 사용 시 기준 연도를 반드시 확인하시기 바랍니다.
데이터셋 개요
항목
내용
총 데이터 수
34,418개
Train 분할
약 30,976개 (90%)
Validation 분할
약 3,442개 (10%)
언어
한국어
형식
Instruction-Input-Output (Alpaca 형식)
기준
K-IFRS (2026년 5월 24일자)
라이선스
CC BY-NC-SA 4.0
데이터 구조 (Data Fields)
각 데이터는… See the full description on the dataset page: https://huggingface.co/datasets/sonsdf/k-ifrs-qa-dataset.ifc-bim-gemma3-subset-1k
IFC-BIM Gemma3 Training Subset (1K Examples)
A 1,000-example subset of IFC/BIM Q&A data formatted for Gemma-3 fine-tuning with Unsloth.
Quick Start
from datasets import load_dataset
# Load dataset
dataset = load_dataset("your-username/ifc-bim-gemma3-subset-1k")
# View first example
print(dataset["train"][0])
Dataset Structure
ShareGPT format with quality scores:
conversations: List of human/gpt exchanges
source: Data origin
score: Quality rating… See the full description on the dataset page: https://huggingface.co/datasets/Dietmar2020/ifc-bim-gemma3-subset-1k.ifc-bim-high-quality-alpaca
IFC BIM High-Quality Dataset (Alpaca Format)
Dataset Description
This is a high-quality, curated dataset for training language models on IFC (Industry Foundation Classes) and BIM (Building Information Modeling) tasks. The dataset has been filtered for quality and is provided in the Alpaca instruction-following format.
Dataset Summary
Total entries: 42,680
Format: Alpaca (instruction, input, output)
Language: English
Domain: IFC/BIM technical documentation and… See the full description on the dataset page: https://huggingface.co/datasets/Dietmar2020/ifc-bim-high-quality-alpaca.NILE-IFT-DatasetHere are the IFT datasets for the EMNLP 2025 Main paper NILE.
These include the Alpaca dataset (release_nile_alpaca_dataset.json) and the sampled OpenOrca dataset (release_nile_orca_dataset.json), both revised by the NILE framework.
ifpri-ai-documents
GAIA / GARDIAN-CIGI Agricultural Research Corpus
This dataset contains 21,726 agricultural research documents extracted from the GARDIAN repository and processed through the CIGI pipeline.
Dataset Overview
Property
Value
Total Documents
21,726
Total Size
623.27 MB
Total Tokens
85,359,442
Total Pages
0
Languages
25
Unique Keywords
7,127
Resource Types
20
Date Generated
2026-07-31 02:55:20
Language Distribution… See the full description on the dataset page: https://huggingface.co/datasets/CGIAR/ifpri-ai-documents.Qwen3.5-reasoning-700x
Dataset Card (Qwen3.5-reasoning-700x)
Dataset Summary
Qwen3.5-reasoning-700x is a high-quality distilled dataset.
This dataset uses the high-quality instructions constructed by Alibaba-Superior-Reasoning-Stage2 as the seed question set. By calling the latest Qwen3.5-27B full-parameter model on the Alibaba Cloud DashScope platform as the teacher model, it generates high-quality responses featuring long-text reasoning processes (Chain-of-Thought). It covers several major… See the full description on the dataset page: https://huggingface.co/datasets/iffrce/Qwen3.5-reasoning-700x.reasoning-sft-IF_multi_constraints_upto5
reasoning-sft-IF_multi_constraints_upto5
Instruction-following dataset with multi-constraint prompts (up to 5 constraints), paired with reasoning responses generated.
Format
Each row has three columns:
input — list of dicts [{"role": "user", "content": "..."}, ...]
response — model response string (includes <think> reasoning block)
category — constraint category label
Usage
import random
import pyarrow.parquet as pq
from huggingface_hub import hf_hub_download… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/reasoning-sft-IF_multi_constraints_upto5.my-distiset-821455fd
Dataset Card for my-distiset-821455fd
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/Ifraaaa/my-distiset-821455fd/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/Ifraaaa/my-distiset-821455fd.