datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Safety-Toxicity-Detection
SEA Toxicity Detection
SEA Toxicity Detection evaluates a model's ability to identify toxic content such as hate speech and abusive language in text. It is sampled from MLHSD for Indonesian, TTD for Thai, and ViHSD for Vietnamese.
Supported Tasks and Leaderboards
SEA Toxicity Detection is designed for evaluating chat or instruction-tuned large language models (LLMs). It is part of the SEA-HELM leaderboard from AI Singapore.
Languages
Indonesian (id)
Thai… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/Safety-Toxicity-Detection.PolygloToxicityPrompts
PolygloToxicityPrompts
Dataset Summary
A multilingual toxicity evaluation benchmark curated from web text.
We prepared 3 splits: ptp-full, ptp-small, and wildchat containining 25K, 5K and 1K prompts per language respectively.
The wildchat split is created using AI2's WildChat dataset.
How do I download this?
Using 🤗 Datasets
from datasets import load_dataset
# English only
dataset =… See the full description on the dataset page: https://huggingface.co/datasets/ToxicityPrompts/PolygloToxicityPrompts.from-one-to-many-toxicity-mitigation
From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models
[arxiv][code][data]
Data accompanying the paper "From One to Many: Expanding the Scope of Toxicity Mitigation in Language Models" accepted to ACL Findings 2024.
Abstract: To date, toxicity mitigation in language models has almost entirely been focused on single-language settings. As language models embrace multilingual capabilities, it’s crucial our safety measures keep pace. Recognizing this research… See the full description on the dataset page: https://huggingface.co/datasets/luizapzbn/from-one-to-many-toxicity-mitigation.task1720_civil_comments_toxicity_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1720_civil_comments_toxicity_classification
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1720_civil_comments_toxicity_classification.task137_detoxifying-lms_classification_toxicity
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task137_detoxifying-lms_classification_toxicity
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task137_detoxifying-lms_classification_toxicity.curatorkit-testrun-Toxicity
curatorkit-testrun-Toxicity
Built using CuratorKIT — provenance-grounded curation and synthesis for LLM post-training.
Method
curation
Backend
—
Model
—
Formats
alpaca
Artifact
dataset
Published
2026-08-30 06:00 UTC
Usage
from datasets import load_dataset
ds = load_dataset("ram-lexsi/curatorkit-testrun-Toxicity", "alpaca")
real-toxicity-prompts-liteThis is a fork of the original RealToxicityPrompts dataset that contains a much smaller subset of the 100k prompts.
Subsets:
50_pct: This subset contains all the challenging prompts + 50% of the full RealToxicityPrompts size sampled from the other prompts.
10_pct: This subset contains all the challenging prompts + 10% of the full RealToxicityPrompts size sampled from the other prompts.
Please refer to the original dataset for the Dataset Card.
auditkit-testrun-metrics-toxicity
auditkit-testrun-metrics-toxicity
Built using AuditKIT — evaluate any model on any dataset and any task.
Method
evaluate
Model
<auditkit.model.vllm_gen.VLLMModel object at 0x7cf12bcbcd70>
Artifact
run
Published
2026-09-01 14:32 UTC
Usage
from datasets import load_dataset
ds = load_dataset("ram-lexsi/auditkit-testrun-metrics-toxicity")
real-toxicity-prompts_benchmarkRealToxicityPrompts
Dataset Card for Real Toxicity Prompts
Dataset Summary
RealToxicityPrompts is a dataset of 100k sentence snippets from the web for researchers to further address the risk of neural toxic degeneration in models.
Languages
English
Dataset Structure
Data Instances
Each instance represents a prompt and its metadata:
{
"filename":"0766186-bc7f2a64cb271f5f56cf6f25570cd9ed.txt",
"begin":340,
"end":564,
"challenging":false… See the full description on the dataset page: https://huggingface.co/datasets/ToxicityPrompts/RealToxicityPrompts.rag-context-toxicity
RAG Context Toxicity Dataset
Overview
The RAG Context Toxicity Dataset is a reproducibility package accompanying a study on how knowledge base composition affects toxicity in Retrieval-Augmented Generation (RAG) systems.
It provides curated knowledge bases and structured evaluation prompts to systematically examine how different retrieval contexts influence the safety and tone of large language model outputs.
The dataset supports controlled experimentation across three… See the full description on the dataset page: https://huggingface.co/datasets/rag-llm-toxicity/rag-context-toxicity.