{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"realtoxicityprompts","slug":"realtoxicityprompts","name":"RealToxicityPrompts","type":"dataset","url":"https://allenai.org/data/real-toxicity-prompts","page_url":"https://unfragile.ai/realtoxicityprompts","categories":["testing-quality","model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"realtoxicityprompts__cap_0","uri":"capability://safety.moderation.multi.dimensional.toxicity.scoring.for.prompt.completion.pairs","name":"multi-dimensional toxicity scoring for prompt-completion pairs","description":"Provides pre-computed toxicity scores across 8 independent dimensions (toxicity, severe_toxicity, threat, insult, identity_attack, profanity, sexually_explicit, flirtation) for 99.4k prompt-continuation pairs extracted from web text. Each dimension is scored on a continuous [0, 1] scale, enabling fine-grained analysis of different toxicity manifestations rather than binary toxic/non-toxic classification. Scores are pre-generated via an undocumented methodology and stored in Parquet format with source document tracking via filename and character offsets.","intents":["Measure which specific toxicity dimensions a language model tends to generate when given prompts","Evaluate whether a model's completions exceed baseline toxicity levels across multiple harm categories","Identify patterns in how different types of toxicity co-occur in model outputs","Benchmark model safety improvements by tracking changes in toxicity scores across dimensions over time"],"best_for":["ML researchers evaluating language model safety and toxicity generation patterns","Model developers implementing toxicity mitigation strategies and measuring effectiveness","Safety teams building evaluation benchmarks for large language models","Organizations conducting comparative toxicity analysis across model families"],"limitations":["Toxicity scoring methodology is undocumented — cannot verify score validity, calibration, or reproduce scores with alternative methods","No inter-annotator agreement metrics or confidence intervals provided if scores are human-generated","Dimension scores appear independent with no documented correlation analysis or joint probability distributions","Score generation date unknown; potential staleness relative to current web toxicity patterns","No explanation of what constitutes 'flirtation' as a toxicity dimension or how it differs from sexually_explicit"],"requires":["Python 3.6+ with Hugging Face Datasets library (datasets>=2.0.0)","Parquet reader (pyarrow or pandas with parquet support)","External toxicity scoring model to evaluate generated continuations (not provided)","Language model for generating completions to evaluate (external requirement)","~500MB disk space for full dataset download"],"input_types":["structured tabular data (Parquet format)","text prompts (English, sentence-level)"],"output_types":["structured data (prompt-continuation pairs with 8-dimensional toxicity scores)","float values (toxicity scores in [0, 1] range per dimension)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"realtoxicityprompts__cap_1","uri":"capability://data.processing.analysis.prompt.continuation.pair.dataset.for.toxicity.evaluation","name":"prompt-continuation pair dataset for toxicity evaluation","description":"Curated collection of 99.4k sentence-level prompts paired with continuation text, both pre-scored for toxicity across 8 dimensions. Prompts are extracted from web sources and include a boolean 'challenging' flag (purpose undocumented) for potential subset stratification. The dataset structure enables a standard evaluation workflow: feed prompt to a language model, generate continuation, score the generated continuation with an external toxicity model, and compare against the baseline continuation scores provided in the dataset.","intents":["Establish a standardized baseline for comparing toxicity generation across different language models","Evaluate whether a model's completions are more or less toxic than the web-sourced baseline continuations","Identify prompts that reliably elicit toxic outputs from language models for targeted mitigation","Create reproducible evaluation benchmarks for toxicity that other researchers can use for comparison"],"best_for":["Researchers conducting comparative toxicity evaluations across model families and versions","Safety teams establishing toxicity evaluation baselines for internal model development","Benchmark creators building standardized LLM evaluation suites","Organizations publishing model cards or safety reports requiring toxicity metrics"],"limitations":["99.4k rows is modest for modern LLM evaluation — insufficient for stratified analysis across demographic groups or toxicity types","Source composition undefined: no specification of domains, time period, quality filtering, or language distribution","No validation/test split documented; single monolithic dataset without recommended train/eval partitioning","Prompt-continuation structure assumes single-turn completion; does not support dialogue, instruction-following, or multi-turn evaluation","No demographic breakdown or toxicity pattern analysis by source domain, topic, or user group","Continuation text is from web sources, not model-generated — baseline may not reflect actual model behavior distribution"],"requires":["Hugging Face Datasets library (datasets>=2.0.0) or equivalent Parquet reader","Python 3.6+","External toxicity scoring model (e.g., Perspective API, Detoxify, or custom classifier)","Language model for generating completions to evaluate","~500MB disk space for full dataset"],"input_types":["structured tabular data (Parquet format with nested structs)"],"output_types":["text (prompts and continuations)","structured data (prompt-continuation pairs with toxicity scores)","float values (8-dimensional toxicity scores per pair)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"realtoxicityprompts__cap_2","uri":"capability://data.processing.analysis.source.traceable.toxicity.data.with.document.offsets","name":"source-traceable toxicity data with document offsets","description":"Each prompt-continuation pair includes filename and character offset metadata (begin/end fields) pointing to the original source document within the web text corpus. This enables researchers to trace toxicity scores back to their source context, filter by source domain, or exclude specific sources from evaluation. The offset-based design allows reconstruction of surrounding context if needed, supporting deeper analysis of how toxicity manifests in broader document context rather than in isolation.","intents":["Trace toxicity scores back to original source documents for validation or context analysis","Filter evaluation dataset by source domain to test model toxicity on specific content types","Exclude problematic sources or domains from evaluation if source quality issues are discovered","Analyze toxicity patterns relative to document context by reconstructing surrounding text"],"best_for":["Researchers validating toxicity scores by examining original source context","Safety teams filtering evaluation data by source domain or quality criteria","Organizations conducting domain-specific toxicity analysis (e.g., news vs. social media)","Teams investigating whether toxicity scores are artifacts of extraction vs. genuine content toxicity"],"limitations":["Original source documents are not provided — offsets are only useful if you have access to the source corpus","No mapping provided between filename and source domain, publication date, or content type","Character offsets may become invalid if source documents are updated or removed","No documented method for reconstructing full document context from offsets","Source corpus composition is undocumented — unclear what domains, time periods, or quality filters were applied"],"requires":["Access to original source documents (not provided with dataset)","Mapping between filenames and source URLs or document identifiers (not provided)","Text processing tools to reconstruct context from character offsets"],"input_types":["structured metadata (filename, begin, end integers)"],"output_types":["text (reconstructed context if original documents are available)","structured data (source document identifiers and offset ranges)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"realtoxicityprompts__cap_3","uri":"capability://data.processing.analysis.challenging.prompt.subset.identification","name":"challenging prompt subset identification","description":"Dataset includes a boolean 'challenging' flag on each record, presumably identifying a subset of prompts that are harder to evaluate or more likely to elicit toxic outputs. The exact semantics of 'challenging' are undocumented, but the flag enables stratified analysis or filtering to focus evaluation on difficult cases. This allows researchers to separately analyze model behavior on routine vs. challenging prompts, potentially revealing failure modes that aggregate metrics would obscure.","intents":["Isolate evaluation to challenging prompts that are more likely to reveal model toxicity vulnerabilities","Compare model performance on routine vs. challenging prompts to identify specific failure modes","Conduct stratified analysis to ensure toxicity mitigation strategies work across difficulty levels","Build harder evaluation benchmarks by filtering to challenging prompts only"],"best_for":["Safety researchers identifying model vulnerabilities through adversarial or challenging prompts","Teams building harder evaluation benchmarks for toxicity","Organizations conducting stratified toxicity analysis across prompt difficulty levels"],"limitations":["Semantics of 'challenging' are completely undocumented — unclear what makes a prompt challenging (length, ambiguity, adversarial intent, etc.)","No explanation of how challenging prompts were identified or selected","No distribution statistics provided (e.g., what percentage of prompts are marked challenging)","No correlation analysis between challenging flag and toxicity scores — unclear if challenging prompts are actually more toxic","Cannot regenerate or extend challenging subset without understanding selection criteria"],"requires":["Understanding of what 'challenging' means in the context of toxicity evaluation (undocumented)"],"input_types":["boolean flag (challenging: true/false)"],"output_types":["filtered dataset (subset of prompts where challenging=true)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"realtoxicityprompts__cap_4","uri":"capability://data.processing.analysis.hugging.face.datasets.integration.with.multiple.access.patterns","name":"hugging face datasets integration with multiple access patterns","description":"Dataset is hosted on Hugging Face Datasets platform and accessible via multiple interfaces: Python API (datasets.load_dataset), SQL Console for querying, Dataset Viewer web interface, and direct Parquet download. This multi-modal access enables integration into various workflows without requiring custom data pipelines. The Parquet format with nested struct schema (prompt and continuation as objects containing text and 8 toxicity scores) supports efficient columnar storage and selective field loading.","intents":["Load dataset into Python evaluation scripts using standard Hugging Face Datasets API","Query specific subsets of data using SQL without downloading the full dataset","Explore dataset structure and sample records via web interface without coding","Download raw Parquet files for integration into custom data pipelines or non-Python environments"],"best_for":["Python-based ML researchers using Hugging Face ecosystem","Teams building evaluation pipelines with standard data loading patterns","Organizations needing SQL-based data exploration without custom tooling","Non-technical stakeholders exploring dataset via web interface"],"limitations":["Hugging Face Datasets library dependency required for Python API access","SQL Console access may have rate limits or query complexity restrictions (not documented)","Parquet nested struct schema requires compatible reader — some tools may not handle nested structures","No streaming API documented — full dataset must be downloaded or queried entirely","Web interface limited to preview/exploration; not suitable for large-scale analysis"],"requires":["Python 3.6+ with datasets library (datasets>=2.0.0) for API access","Parquet reader (pyarrow, pandas, or equivalent) for direct file access","Internet connection for Hugging Face Datasets access","~500MB disk space for full dataset download"],"input_types":["none (data source)"],"output_types":["Python Dataset object (via datasets.load_dataset)","Parquet files (via direct download)","SQL query results (via Hugging Face SQL Console)","JSON preview (via Dataset Viewer)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"realtoxicityprompts__cap_5","uri":"capability://tool.use.integration.hugging.face.datasets.api.integration.for.standardized.access","name":"hugging face datasets api integration for standardized access","description":"Dataset is hosted on Hugging Face Hub and accessible via the standard `datasets` library API (load_dataset('allenai/real-toxicity-prompts')), providing automatic Parquet parsing, caching, streaming, and standard Python data structures. This integration eliminates custom data loading code and enables seamless integration with Hugging Face ecosystem tools (transformers, evaluate, etc.).","intents":["Load the dataset with a single line of Python code without manual file handling","Stream large datasets without loading entire corpus into memory","Cache downloaded data locally for repeated access","Integrate with Hugging Face model evaluation pipelines and tools"],"best_for":["Python developers using Hugging Face ecosystem tools","Researchers building evaluation pipelines with transformers library","Teams already using Hugging Face Hub for model hosting and evaluation"],"limitations":["Requires Hugging Face datasets library installation and Python 3.6+ environment","Streaming mode requires stable internet connection; offline access requires pre-download","No API endpoints or REST access; Python library is only access method","Dataset versioning follows Hugging Face Hub conventions; no explicit version pinning in dataset itself"],"requires":["Python 3.6+","Hugging Face datasets library (pip install datasets)","Internet connection for initial download (unless pre-cached)"],"input_types":["dataset identifier string ('allenai/real-toxicity-prompts')"],"output_types":["Hugging Face Dataset object with dict-like access to records"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"realtoxicityprompts__cap_6","uri":"capability://safety.moderation.toxicity.based.model.evaluation.benchmarking","name":"toxicity-based model evaluation benchmarking","description":"Enables systematic benchmarking of language models by measuring toxicity in their completions when given prompts from the corpus. Researchers generate completions for all 99.4k prompts, score them using the same 8-dimensional toxicity classifier, and aggregate metrics (mean toxicity per dimension, percentage of toxic outputs, etc.) to create comparative benchmarks across models.","intents":["Compare toxicity propensity across different language models (GPT-3 vs. BERT vs. custom models)","Measure whether model fine-tuning or instruction-tuning reduces toxicity","Establish baseline toxicity metrics for model safety evaluation","Track toxicity improvements over model versions or training iterations"],"best_for":["Model developers evaluating safety of new model versions","Researchers comparing toxicity across model architectures or training approaches","Teams implementing model safety benchmarks and leaderboards"],"limitations":["Requires external toxicity classifier to score model outputs—dataset does not provide classifier; must match or replicate the undocumented classifier used for dataset scores","Benchmarking requires running inference on 99.4k prompts, which is computationally expensive for large models","Toxicity scores are relative to classifier quality; classifier biases or errors propagate to benchmark results","No guidance on aggregation methodology (mean? percentile? weighted by prompt difficulty?)","Benchmarks are snapshot-based; toxicity may vary with model temperature, sampling method, or other generation parameters"],"requires":["Language model capable of generating completions","Toxicity classifier (external; specification undocumented)","Computational resources for 99.4k inference passes","Python 3.6+ with datasets library and model inference framework"],"input_types":["prompts from dataset (text strings)"],"output_types":["toxicity scores for model-generated continuations (float values across 8 dimensions)","aggregated benchmark metrics (mean, percentile, percentage toxic)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"realtoxicityprompts__headline","uri":"capability://data.processing.analysis.toxicity.evaluation.dataset.for.language.models","name":"toxicity evaluation dataset for language models","description":"A comprehensive dataset of 100K sentence-level prompts with toxicity scores, designed for evaluating and mitigating toxic text generation in AI models, making it essential for researchers and developers focused on ethical AI.","intents":["best dataset for evaluating language model toxicity","dataset for training toxicity detection models","how to mitigate toxic AI outputs","toxic language analysis dataset","dataset for NLP toxicity research"],"best_for":["NLP researchers","AI developers","data scientists"],"limitations":["static dataset","contextual limitations"],"requires":["basic understanding of NLP","data manipulation skills"],"input_types":["text prompts"],"output_types":["toxicity scores"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"low","permissions":["Python 3.6+ with Hugging Face Datasets library (datasets>=2.0.0)","Parquet reader (pyarrow or pandas with parquet support)","External toxicity scoring model to evaluate generated continuations (not provided)","Language model for generating completions to evaluate (external requirement)","~500MB disk space for full dataset download","Hugging Face Datasets library (datasets>=2.0.0) or equivalent Parquet reader","Python 3.6+","External toxicity scoring model (e.g., Perspective API, Detoxify, or custom classifier)","Language model for generating completions to evaluate","~500MB disk space for full dataset"],"failure_modes":["Toxicity scoring methodology is undocumented — cannot verify score validity, calibration, or reproduce scores with alternative methods","No inter-annotator agreement metrics or confidence intervals provided if scores are human-generated","Dimension scores appear independent with no documented correlation analysis or joint probability distributions","Score generation date unknown; potential staleness relative to current web toxicity patterns","No explanation of what constitutes 'flirtation' as a toxicity dimension or how it differs from sexually_explicit","99.4k rows is modest for modern LLM evaluation — insufficient for stratified analysis across demographic groups or toxicity types","Source composition undefined: no specification of domains, time period, quality filtering, or language distribution","No validation/test split documented; single monolithic dataset without recommended train/eval partitioning","Prompt-continuation structure assumes single-turn completion; does not support dialogue, instruction-following, or multi-turn evaluation","No demographic breakdown or toxicity pattern analysis by source domain, topic, or user group","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=realtoxicityprompts","compare_url":"https://unfragile.ai/compare?artifact=realtoxicityprompts"}},"signature":"BI00wE+osSAg9SDWr1lK49REUND8PZ37jQxeWCjbYcP9OGECsRRJguQn8eVazOeHeAlNcke3pKzYjJispIvXBw==","signedAt":"2026-06-20T23:03:38.461Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/realtoxicityprompts","artifact":"https://unfragile.ai/realtoxicityprompts","verify":"https://unfragile.ai/api/v1/verify?slug=realtoxicityprompts","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}