datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.LLM_for_Dietary_Recommendation_System
Chat_GPT_for_Nutritional_Recommendation_System
This repository contains the code, 50 different patient profiles, and respective Chat-GPT responses with nutritional recommendations and sample diet plans. Patient profiles contain easy, medium, and complex cases and various diseases. The project aims to evaluate the application of large language models for nutritional recommendation systems.
Responses were evaluated based on personalization, consistency with evidence-based… See the full description on the dataset page: https://huggingface.co/datasets/issai/LLM_for_Dietary_Recommendation_System.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.diet-planning-evaluation-20250531-140238
Diet Planning Evaluation Results
Dataset Summary
This dataset contains evaluation results for diet planning model responses.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English
Dataset Structure
Data Instances
Each instance contains a model response and its evaluation metrics.
Data Fields
Full Prompt: object
Model Response: object
Desired Response: object
Normalized_Edit_Distance: float64… See the full description on the dataset page: https://huggingface.co/datasets/alexjk1m/diet-planning-evaluation-20250531-140238.diet-planning-evaluation-20250531-140436
Diet Planning Evaluation Results
Dataset Description
This dataset contains evaluation results for diet planning model responses.
Dataset Structure
The dataset is stored in parquet format for efficient loading and pagination.