datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
IntentGrasp
IntentGrasp: A Comprehensive Benchmark for Intent Understanding
Paper: https://arxiv.org/abs/2605.06832
Authors: Yuwei Yin, Chuyuan Li, Giuseppe Carenini
Institute: UBC NLP Group, Department of Computer Science, University of British Columbia
Keywords: Intent Understanding, Dataset, Benchmark, LLM, Evaluation, Intentional Fine-Tuning
Abstract:
Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language… See the full description on the dataset page: https://huggingface.co/datasets/yuweiyin/IntentGrasp.Home-Assistant-requests-for-intent-detection-and-function-recognition
Home Assistant Requests V2 Dataset
This dataset contains a list of requests and responses for a user interacting with a personal assistant that controls an instance of Home Assistant.
The updated V2 of the dataset is now multilingual, containing data in English, German, French, Spanish, and Polish. The dataset also contains multiple "personalities" for the assistant to respond in, such as a formal assistant, a sarcastic assistant, and a friendly assistant. Lastly, the dataset has… See the full description on the dataset page: https://huggingface.co/datasets/DaftP/Home-Assistant-requests-for-intent-detection-and-function-recognition.aura-implicit-intent
AURA Implicit-Intent Benchmark (AURATown)
A small, author-authored evaluation suite for studying implicit-need
surfacing by situated LLM agents. A situated query like "Where is Lin Wei?"
often encodes more than its literal content — the user may also want to know
whether Lin Wei is available, in a good mood, or worth interrupting now.
This benchmark separates the literal answer (readable from public scene
state) from the implicit need (which requires private/hidden state),
and… See the full description on the dataset page: https://huggingface.co/datasets/innovation64/aura-implicit-intent.Adversarial-Agent-Intent-Safety-Analysis-240K
Adversarial Agent Intent Safety Analysis 240K
Abstract
The Adversarial-Agent-Intent-Safety-Analysis-240K is a deterministically structured dataset featuring 242,454 context-rich adversarial prompts and safety evaluations. Engineered strictly for training frontier command-and-control models, guardrail classifiers, and red-teaming agents, it encourages models to parse multi-layered intention across 126 critical risk vectors.
This design trains models to decouple the surface… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Adversarial-Agent-Intent-Safety-Analysis-240K.agent-orchestration-intents-dataset
Agent Orchestration Dataset
A dataset for training and evaluating intelligent orchestrator models that route user requests to specialist agents with high-level intent classification.
Dataset Description
This dataset contains conversational examples where an orchestrator must analyze user requests and determine which specialist agents should handle them. Each sample includes intent labels for additional classification tasks.
Key Features:
Multi-agent routing scenarios… See the full description on the dataset page: https://huggingface.co/datasets/rescommons/agent-orchestration-intents-dataset.Typo_Intent_OS
Dataset Description
3383 pairs of questions and answers about the Web3 knowledge base, used for training and testing AI models applied to Web3 scenarios.
Data Format
prompt: AI-generated question.
completion: The candidate answer selected by the majority of users.
Example:
{
"prompt": "What is a primary advantage of using a decentralized finance (DeFi) platform?",
"completion": "Direct peer-to-peer transactions without intermediaries."
}
Data Source
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/typox-ai/Typo_Intent_OS.intentspec-examples
IntentSpec Examples
Synthetic examples showing how raw product evidence can be transformed into agent-ready IntentSpecs.
Each row contains customer evidence, a weak implementation prompt, and a stronger structured IntentSpec with objective, outcomes, constraints, and edge cases. The examples are designed to teach the difference between asking an AI coding agent to perform a task and giving it the product intent it should preserve while building.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/pathmode/intentspec-examples.