datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CANADA_ACT_REGULATION_QA
Canadian Acts and Regulation QA
source- https://laws-lois.justice.gc.ca/eng/XML/Legis.xml
model_name="gemini-1.5-flash-latest" with 1 million context length,
First summarize the text scrapped text from xml tree of urls using gemini.
then generate QA from sumarised text.
Performance of Gemini was way way better than GPT-4.
Fitering was done based on Heuristics after rigrous analysis because llms were not always accurate.
summary_prompt_template= """
You'r legal expert… See the full description on the dataset page: https://huggingface.co/datasets/Guggu/CANADA_ACT_REGULATION_QA.parking_regulationsThis is a dataset of NYC parking regulations exported from OpenData on April 22, 2025.
CFR-Title-41-Federal-Travel-Regulation
Federal Travel Regulation
Maintainer: Terry Eppler
Owner: US Federal Government
Dataset Summary
This dataset contains document-grounded question-and-answer records based on the Federal Travel Regulation, as reproduced in the source volume of title 41 of the Code of Federal Regulations.
The Federal Travel Regulation establishes government-wide policies governing official civilian travel and relocation at Federal expense. It addresses temporary duty travel… See the full description on the dataset page: https://huggingface.co/datasets/leeroy-jankins/CFR-Title-41-Federal-Travel-Regulation.gcc-heat-illness-regulations-2026
Canonical landing page: https://www.smartqhse.com/datasets/gcc-heat-illness-regulations-2026
GCC Heat Illness Regulations 2026
Consolidated occupational heat-illness work-stoppage rules across all six GCC countries — UAE, Saudi Arabia, Qatar, Oman, Bahrain, Kuwait. Includes exact summer ban window dates, daily prohibited-work hours, WBGT triggers, employer penalties, and regulator authority. Sourced from each country's labour ministry primary regulations. Updated annually.… See the full description on the dataset page: https://huggingface.co/datasets/SmartQHSE/gcc-heat-illness-regulations-2026.Regulations_MOF
Overview
The question set is developed to assess LLMs' ability to answer questions about the licensing requirements outlined in the Model Openness Framework (MOF). It is created for the MOF licenses task at Regulations Challenge @ COLING 2025. The MOF evaluates and classifies the completeness and openness of machine learning models. The MOF decomposes the model development lifecycle into 17 components, each with specific licensing requirements to ensure openness. LLMs can help the… See the full description on the dataset page: https://huggingface.co/datasets/SecureFinAI-Lab/Regulations_MOF.pokemon_battle_team_dataset_regulation_fポケモン(VGC)のレギュレーションF ルールにおける選出データを記録したデータセットです。
YouTube 上で公開状態になっていた対戦配信から選出データを収集しました。trainer_id がそれぞれ配信を行っていた方に対応します。
なお、trainer_id 13 は本データセット作者が行った対戦データを示しています。
こちらのデータを用いて、2024年5月にリモートポケモン学会というコミュニティで発表を行いました。
配信 https://youtu.be/4Op-JvVEQ10?t=3479
スライド https://speakerdeck.com/fufufukakaka/ji-jie-xue-xi-woyong-itapokemondui-zhan-xuan-chu-yu-ce
public-policy-regulation-compliance-outcome-coherence-risk-v0.1What this repo is for
Detect when rules exist but outcomes don’t change.
You use it to flag
high compliance but no outcome improvement
low inspection capacity masking risk
rule complexity blocking enforcement
enforcement collapse before public failure
Regulations_QA
Overview
This question set aims to assess LLMs' ability to answer questions about financial regulations accurately. It is for the question-answering task at Regulations Challenge @ COLING 2025. The objective is to determine the LLMs’ ability to interpret complex legal and regulatory information and to provide precise and informative answers.
Question answering is a task to assess LLMs' ability to understand and interpret financial regulations. Providing an accurate and reliable… See the full description on the dataset page: https://huggingface.co/datasets/SecureFinAI-Lab/Regulations_QA.Regulations_abbreviation
Overview
The dataset contains stock tickers and acronyms for regulatory terms. It is developed for the abbreviation recognition task at Regulations Challenge @ COLING 2025. This dataset is designed to evaluate and benchmark the performance of LLMs in understanding and generating expansions for abbreviations within the context of regulatory and compliance documentation. It provides a collection of abbreviations commonly encountered in regulatory texts, along with their full forms.… See the full description on the dataset page: https://huggingface.co/datasets/SecureFinAI-Lab/Regulations_abbreviation.clinical-glucose-regulation-instability-v0.1
clinical-glucose-regulation-instability-v0.1
What this dataset does
This dataset evaluates whether models can detect instability in glucose regulation.
Each row represents a simplified glucose control scenario observed across three time points.
The task is to determine whether metabolic glucose regulation remains stable or is moving toward regulatory instability.
Core stability idea
Glucose stability depends on feedback between insulin signaling, hepatic… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-glucose-regulation-instability-v0.1.European-E-commerce-Chatbot-Unsolicited-Email-Regulation-Harmless
Dataset Card for Unsolicited Email Regulation Harmless
Description
The test set is designed for evaluating a European E-commerce Chatbot, specifically focused on the E-commerce industry. The main objective of the test set is to assess the reliability of the chatbot's performance, ensuring it can provide accurate and trustworthy information to users. The test set contains various scenarios that cover harmless interactions commonly encountered in e-commerce, including… See the full description on the dataset page: https://huggingface.co/datasets/rhesis/European-E-commerce-Chatbot-Unsolicited-Email-Regulation-Harmless.Regulations_Link_Retrieval
Overview
This question set is created to assess the ability of LLMs to retrieve and provide exact links to specific regulations. It is for the link retrieval task at Regulations Challenge @ COLING 2025. The objective is to evaluate LLM’s effectiveness in navigating complex legal databases to find and reference the correct documents.
Financial product contracts, financial reports, and compliance documents require references or citations to specific legal provisions. Quickly finding… See the full description on the dataset page: https://huggingface.co/datasets/SecureFinAI-Lab/Regulations_Link_Retrieval.Mood-Regulation-Audio-Reference-Catalog
Mood-Regulation-Audio-Reference-Catalog
Entity Reference
Creator: Inna StoryISNI: 0000 0005 3033 4113Framework: Entity Life Cycle (ELC)Official Hub: innastoryofficial.com
Описание
Данный датасет представляет собой техническую библиотеку аудио-ассетов, спроектированных для прецизионного управления психоэмоциональным состоянием слушателя. Проект базируется на методологии Entity Life Cycle (ELC) и рассматривает музыкальный контент как инженерный… See the full description on the dataset page: https://huggingface.co/datasets/InnaStory/Mood-Regulation-Audio-Reference-Catalog.fda_samd_regulations_golden_test_datasetRegulations_NER
Overview
This question set is created to evaluate LLMs' ability for named entity recognition (NER) in financial regulatory texts. It is developed for a task at Regulations Challege @ COLING 2025. The objective is to accurately identify and classify entities, including organizations, legislation, dates, monetary values, and statistics.
Financial regulations often require supervising and reporting on specific entities, such as organizations, financial products, and transactions, and… See the full description on the dataset page: https://huggingface.co/datasets/SecureFinAI-Lab/Regulations_NER.mersin-university-regulationsRegulations_CDM
Overview
The CDM question set is to assess LLMs’ ability to answer questions related to the Common Domain Model (CDM). It is created for the CDM task at Regulations Challenge @ COLING 2025. CDM is a machine-oriented model for managing the lifecycle of financial products and transactions. It aims to enhance the efficiency and regulatory oversight of financial markets. For this new machine-oriented standard, LLMs can help the financial community understand CDM’s modeling approach, use… See the full description on the dataset page: https://huggingface.co/datasets/SecureFinAI-Lab/Regulations_CDM.Regulations_definition
Overview
This dataset is crafted to evaluate the capability of LLMs to accurately understand and generate definitions for terms commonly used in regulatory and compliance contexts. It is developed for the definition recognition task at Regulations Challenge @ COLING 2025. It includes a curated collection of key terms, along with their definitions as used in regulatory documents.
Accurate and consistent definitions of terms in financial regulations are important in understanding… See the full description on the dataset page: https://huggingface.co/datasets/SecureFinAI-Lab/Regulations_definition.