datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.med-hallucination-detection
Medical hallucination detection
A dataset for training a small model to detect hallucinations in medical answers
and explain why, by checking each answer against the context it should be
grounded in. Each row is a (question, answer, context) triple with a row_type:
not_hallucinated -- the answer is grounded in its context.
hallucinated -- the answer is not (sourced separately; see below).
The not_hallucinated split (this build)
Derived from MedQuAD, a collection… See the full description on the dataset page: https://huggingface.co/datasets/Certops/med-hallucination-detection.urgency_detection_maternal_health_synthetic
Maternal Health Messages for Urgency Detection
This dataset contains ~12.8k synthetic user messages related to
maternal health issues generated using
Gemini-1.5-Flash and verified using
Gemini-1.5-Pro.
This dataset can be used to train models for detecting urgent messges related to maternal health. One such model is the
gemma-2-2b-it-ud model.
The prompts used to generate the dataset are contained in the prompts_for_generating_user_messages.py module.
Motivation… See the full description on the dataset page: https://huggingface.co/datasets/IDinsight/urgency_detection_maternal_health_synthetic.med-hallucination-detection-unfiltered
Medical hallucination detection (unfiltered)
A dataset for training a small model to detect hallucinations in medical answers
and explain why, by checking each answer against the context it should be
grounded in. Each row is a (question, answer, context) triple labelled row_type.
This is the unfiltered union of two sources: 7,464 grounded positives and
10,000 planted-hallucination negatives. It is the raw pool before sampling and
judging -- the downstream step samples from here… See the full description on the dataset page: https://huggingface.co/datasets/Certops/med-hallucination-detection-unfiltered.error-detection-positives
error-detection-positives
This dataset is part of the PARC (Premise-Annotated Reasoning Collection) and contains mathematical reasoning problems with error annotations. This dataset combines positives samples from multiple domains.
Domain Breakdown
gsm8k: 50 samples
math: 53 samples
metamathqa: 93 samples
orca_math: 96 samples
Features
Each example contains:
data_source: The domain/source of the problem (gsm8k, math, metamathqa, orca_math)
question: The… See the full description on the dataset page: https://huggingface.co/datasets/PARC-DATASETS/error-detection-positives.Conversational-Cancer-Lung-Detection
Conversational Cancer Lung Detection Dataset
This dataset, Conversational Cancer Lung Detection, is a conversationally structured dataset derived from the original Lung Cancer Detection dataset by Jillani Soft Tech on Kaggle. It has been transformed to simulate medical records in a conversational format, enabling AI applications to interact in a question-answer style format about lung cancer detection.
Dataset Overview
The Conversational Cancer Lung Detection dataset… See the full description on the dataset page: https://huggingface.co/datasets/BrokenSoul/Conversational-Cancer-Lung-Detection.error-detection-negatives
error-detection-negatives
This dataset is part of the PARC (Premise-Annotated Reasoning Collection) and contains mathematical reasoning problems with error annotations. This dataset combines negatives samples from multiple domains.
Domain Breakdown
gsm8k: 57 samples
math: 44 samples
metamathqa: 59 samples
orca_math: 54 samples
Features
Each example contains:
data_source: The domain/source of the problem (gsm8k, math, metamathqa, orca_math)
question: The… See the full description on the dataset page: https://huggingface.co/datasets/PARC-DATASETS/error-detection-negatives.error-detection-positives_perturbed
error-detection-positives_perturbed
This dataset is part of the PARC (Premise-Annotated Reasoning Collection) and contains mathematical reasoning problems with error annotations. This dataset combines positives_perturbed samples from multiple domains.
Domain Breakdown
gsm8k: 48 samples
math: 42 samples
metamathqa: 72 samples
orca_math: 85 samples
Features
Each example contains:
data_source: The domain/source of the problem (gsm8k, math, metamathqa… See the full description on the dataset page: https://huggingface.co/datasets/PARC-DATASETS/error-detection-positives_perturbed.omission-detection-realdata-pilot
Omission Detection — Real-Data Pilot
What is Omission Detection?
Large language models (LLMs) in agentic pipelines often omit information
present in their context window — they fail to surface a relevant fact even
when it is theoretically visible. This dataset captures 372 controlled
trials from the real-data pilot, extending the synthetic sweep to
real-world documents and agent frameworks.
Each trial fetches a document from a real source (PubMed, HAPI FHIR, SEC… See the full description on the dataset page: https://huggingface.co/datasets/Santhiyarajan/omission-detection-realdata-pilot.Encoding-Detection-w-cChardet-DBLICENSE:
I don't care but some info is from cChardet so i just link it's license here.
omission-detection-synthetic
Omission Detection — Synthetic Sweep
What is Omission Detection?
Large language models (LLMs) in agentic pipelines often omit information
present in their context window — they fail to surface a relevant fact even
when it is theoretically visible. This dataset captures 75,876 controlled
trials designed to measure and attribute these omissions across 9 taxonomic
layers (L0–L8).
Each trial generates a synthetic clinical document, embeds a "needle" fact at a… See the full description on the dataset page: https://huggingface.co/datasets/Santhiyarajan/omission-detection-synthetic.Object_detection_dataset