{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"toxigen","slug":"toxigen","name":"ToxiGen","type":"dataset","url":"https://github.com/microsoft/TOXIGEN","page_url":"https://unfragile.ai/toxigen","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"toxigen__cap_0","uri":"capability://text.generation.language.adversarial.hate.speech.generation.via.alice.framework","name":"adversarial-hate-speech-generation-via-alice-framework","description":"Generates adversarial toxic text examples using the ALICE (Adversarial Language-model Interaction for Classifier Evasion) framework, which implements a beam search algorithm that combines GPT-3 language model probabilities with toxicity classifier confidence scores to produce fluent text that evades existing hate speech detection systems. The framework iteratively refines candidates by weighting both language model likelihood and adversarial objectives, enabling discovery of subtle, implicit hate speech without explicit slurs.","intents":["Generate challenging adversarial examples to stress-test hate speech classifiers and identify detection gaps","Create training data for robust toxicity detection models that can catch implicit and subtle forms of hate speech","Discover evasion techniques that bad actors might use to bypass content moderation systems","Benchmark classifier robustness against adversarially-crafted toxic content"],"best_for":["ML researchers building robust hate speech detection systems","content moderation teams evaluating classifier vulnerabilities","safety researchers studying adversarial robustness in NLP"],"limitations":["Requires OpenAI API access and associated costs for GPT-3 inference during generation","Beam search algorithm adds computational overhead; generation time scales with beam width and sequence length","Generated examples may contain offensive content by design; requires careful ethical review before deployment","Classifier integration limited to HateBERT/RoBERTa; extending to other classifiers requires custom scoring implementations"],"requires":["Python 3.8+","PyTorch 1.10.2+","Transformers 4.12.3+","OpenAI API key for GPT-3 access","Pre-trained toxicity classifier (HateBERT or RoBERTa) loaded locally or via Hugging Face"],"input_types":["human-written toxic demonstrations (plain text, one per line)","target minority group identifiers (13 supported groups)","classifier model weights and configuration","beam search hyperparameters (beam width, scoring weights)"],"output_types":["generated toxic text examples (plain text)","adversarial scores and classifier confidence metrics","structured dataset with annotations (JSON/CSV format)"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_1","uri":"capability://text.generation.language.demonstration.based.prompt.generation.for.minority.groups","name":"demonstration-based-prompt-generation-for-minority-groups","description":"Converts human-written toxic demonstrations into structured few-shot prompts that guide GPT-3 to generate similar toxic content across 13 minority groups. The system uses a configurable prompt template that includes human examples as in-context demonstrations, enabling controlled generation of group-specific toxic statements without requiring manual prompt engineering for each group.","intents":["Scale toxic example generation across multiple minority groups using a single set of human demonstrations","Create consistent, reproducible prompts for generating comparable toxic content across different target groups","Control the number and diversity of in-context examples per prompt to tune generation behavior","Generate baseline toxic datasets before applying adversarial refinement via ALICE"],"best_for":["dataset creators building balanced toxicity corpora across multiple demographic groups","researchers studying how toxicity patterns vary across different target groups","teams needing rapid iteration on prompt design without manual rewriting"],"limitations":["Requires high-quality human demonstrations as seeds; poor seed examples propagate through generated dataset","Generation quality depends on GPT-3 prompt engineering; template changes may significantly alter output distribution","Limited to 13 pre-defined minority groups; extending to new groups requires new human demonstrations","No built-in deduplication; generated examples may contain near-duplicates across groups"],"requires":["Python 3.8+","Human-written demonstrations stored in demonstrations/ directory (plain text format)","OpenAI API key for GPT-3 access","Configurable prompt template (provided in make_prompts.py)"],"input_types":["human demonstrations (plain text, one toxic statement per line)","target group identifiers (13 supported minority groups)","prompt configuration (number of examples per prompt, template format)"],"output_types":["structured prompts (JSON or text format)","mapping of prompts to target groups","metadata about demonstration sources and group assignments"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_10","uri":"capability://data.processing.analysis.evaluation.metrics.and.classifier.robustness.benchmarking","name":"evaluation-metrics-and-classifier-robustness-benchmarking","description":"Provides evaluation metrics for assessing classifier robustness on generated adversarial datasets, including accuracy, precision, recall, F1-score, and adversarial success rate (percentage of generated examples misclassified as benign). The system enables benchmarking of different classifiers on the same adversarial dataset and comparison of robustness across different generation strategies.","intents":["Measure classifier robustness against adversarial toxic examples","Compare robustness across different classifiers (HateBERT, RoBERTa, etc.)","Evaluate how generation parameters affect adversarial success rate","Benchmark classifier performance on implicit vs. explicit toxicity"],"best_for":["researchers evaluating classifier robustness on adversarial datasets","teams comparing different classifiers for deployment","safety researchers studying adversarial robustness in NLP"],"limitations":["Metrics assume binary toxic/benign labels; no support for multi-class toxicity levels","Adversarial success rate depends on classifier threshold; different thresholds may give different results","No statistical significance testing; requires manual analysis of result differences","Metrics don't capture human perception of toxicity; classifier disagreement with humans may not be reflected"],"requires":["Python 3.8+","Generated adversarial dataset with labels","Pre-trained classifier for evaluation","Evaluation script or library (scikit-learn, etc.)"],"input_types":["generated adversarial examples","ground truth labels (toxic/benign)","classifier predictions","classifier confidence scores"],"output_types":["accuracy, precision, recall, F1-score","adversarial success rate (% misclassified)","per-group performance metrics","confusion matrices and ROC curves"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_2","uri":"capability://safety.moderation.pretrained.toxicity.classifier.integration","name":"pretrained-toxicity-classifier-integration","description":"Integrates pre-trained hate speech classifiers (HateBERT, RoBERTa) into the generation pipeline to provide real-time toxicity scoring during beam search. The integration abstracts classifier inference behind a unified interface, enabling the ALICE framework to query classifier confidence scores for candidate text and use those scores as feedback signals to guide adversarial generation.","intents":["Score generated text for toxicity in real-time during adversarial generation","Provide classifier feedback to the beam search algorithm to guide selection of adversarial candidates","Evaluate generated examples against multiple classifiers to ensure robustness","Benchmark classifier performance on generated adversarial datasets"],"best_for":["researchers evaluating classifier robustness against adversarial examples","teams building adversarial training pipelines that require real-time classifier feedback","safety researchers studying classifier vulnerabilities"],"limitations":["Classifier inference adds latency to generation pipeline; scoring each beam candidate requires forward pass through transformer model","Limited to HateBERT and RoBERTa; integrating custom classifiers requires implementing new scoring modules","Classifier performance depends on training data; classifiers may have blind spots for certain types of implicit toxicity","No ensemble scoring by default; using multiple classifiers requires manual aggregation logic"],"requires":["Python 3.8+","Transformers 4.12.3+ library for model loading","PyTorch 1.10.2+ for inference","Pre-trained classifier weights (HateBERT or RoBERTa) from Hugging Face Model Hub","GPU recommended for real-time scoring during beam search"],"input_types":["candidate text strings (variable length)","classifier model identifier (HateBERT or RoBERTa)","batch of candidates for scoring"],"output_types":["toxicity confidence scores (0-1 range)","per-class probabilities (toxic vs. benign)","scoring metadata (model version, inference time)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_3","uri":"capability://text.generation.language.beam.search.text.generation.with.dual.objectives","name":"beam-search-text-generation-with-dual-objectives","description":"Implements a beam search algorithm that maintains multiple candidate text sequences and scores each candidate using a weighted combination of language model probability (fluency) and classifier confidence (adversarial objective). At each decoding step, the algorithm expands candidates by sampling from the language model, scores all expansions, and retains the top-k candidates based on the combined objective, enabling discovery of text that is both fluent and adversarial.","intents":["Generate fluent, natural-sounding adversarial text rather than grammatically broken or obviously malicious examples","Balance competing objectives (fluency vs. evasion) during generation to produce realistic adversarial examples","Explore the space of possible toxic statements within a constrained beam width","Enable fine-grained control over the fluency-adversarial tradeoff via scoring weights"],"best_for":["researchers generating realistic adversarial examples for classifier robustness evaluation","teams building adversarial training datasets that require natural-sounding toxic content","safety researchers studying the boundary between fluent and adversarial text"],"limitations":["Beam search is greedy and may miss globally optimal solutions; larger beam widths improve quality but increase computation","Scoring weights must be manually tuned; no automatic method for balancing fluency vs. adversarial objectives","Generation time scales linearly with beam width and sequence length; real-time generation may be infeasible for large beams","Requires both language model and classifier access; cannot generate without both components"],"requires":["Python 3.8+","GPT-3 API access for language model probabilities","Pre-trained toxicity classifier (HateBERT or RoBERTa)","Configurable beam width and scoring weights"],"input_types":["prompt text (seed for generation)","beam width (number of candidates to maintain)","scoring weights (fluency vs. adversarial)","maximum sequence length","language model and classifier instances"],"output_types":["generated text sequences (variable length)","per-sequence scores (fluency, adversarial, combined)","generation trace (candidate expansions at each step)"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_4","uri":"capability://data.processing.analysis.structured.dataset.loading.and.distribution","name":"structured-dataset-loading-and-distribution","description":"Provides a standardized interface for loading, organizing, and distributing the generated toxic and benign datasets through Hugging Face Hub. The system structures data with consistent annotations (toxicity labels, target groups, generation method), enables easy filtering and splitting for train/test/validation, and supports multiple serialization formats (JSON, CSV, Parquet) for compatibility with different ML frameworks.","intents":["Load pre-generated ToxiGen datasets directly into ML training pipelines without custom parsing","Filter datasets by target group, toxicity level, or generation method for targeted evaluation","Split datasets into train/validation/test sets with reproducible random seeds","Export datasets in multiple formats for use with different ML frameworks (PyTorch, TensorFlow, scikit-learn)"],"best_for":["ML practitioners training toxicity classifiers on adversarial datasets","researchers benchmarking classifier robustness across different dataset splits","teams integrating ToxiGen data into existing ML pipelines"],"limitations":["Datasets contain offensive content by design; requires careful handling and ethical review before use","Pre-generated datasets are static; custom generation requires running the full ALICE pipeline","Dataset size may be large (millions of examples); loading entire dataset into memory may be infeasible on resource-constrained systems","No built-in data augmentation or transformation; custom preprocessing required for specific use cases"],"requires":["Python 3.8+","Hugging Face datasets library","Internet access to download datasets from Hugging Face Hub","Sufficient disk space for dataset storage"],"input_types":["dataset identifier (e.g., 'toxigen-hatebert')","filtering criteria (target group, toxicity threshold, generation method)","split configuration (train/val/test proportions)"],"output_types":["structured dataset objects (Hugging Face Dataset format)","serialized data files (JSON, CSV, Parquet)","metadata and statistics (dataset size, label distribution, group coverage)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_5","uri":"capability://text.generation.language.implicit.toxicity.detection.via.subtle.examples","name":"implicit-toxicity-detection-via-subtle-examples","description":"Generates toxic statements that contain no explicit slurs or profanity but express hateful sentiment through subtle language, innuendo, and implicit bias. The system uses human demonstrations and the ALICE framework to discover linguistic patterns that convey toxicity without triggering keyword-based filters, enabling evaluation of classifiers' ability to detect implicit hate speech that relies on context and coded language.","intents":["Identify blind spots in toxicity classifiers that rely on explicit keyword matching","Create training data for classifiers that can detect implicit and coded hate speech","Evaluate how well classifiers generalize to subtle forms of toxicity not seen during training","Study linguistic patterns used to evade content moderation systems"],"best_for":["content moderation researchers studying evasion techniques and implicit toxicity","teams building next-generation toxicity classifiers that understand context and nuance","safety researchers evaluating classifier robustness against subtle attacks"],"limitations":["Implicit toxicity is subjective and context-dependent; annotation quality depends on human judgment","Generated implicit examples may be less obviously toxic, making evaluation and validation more difficult","Classifiers trained on implicit toxicity may have higher false positive rates on benign content with similar linguistic patterns","No universal definition of 'implicit toxicity'; different annotators may disagree on borderline examples"],"requires":["Python 3.8+","Human demonstrations of implicit toxic statements","Pre-trained toxicity classifier capable of detecting subtle patterns","ALICE framework for adversarial generation"],"input_types":["human demonstrations of implicit toxic statements (no explicit slurs)","target minority groups","classifier for scoring implicit toxicity"],"output_types":["generated implicit toxic statements (plain text)","toxicity scores and classifier confidence","linguistic analysis of evasion patterns"],"categories":["text-generation-language","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_6","uri":"capability://data.processing.analysis.multi.group.toxicity.dataset.generation.across.13.minorities","name":"multi-group-toxicity-dataset-generation-across-13-minorities","description":"Generates balanced toxic and benign datasets targeting 13 distinct minority groups (e.g., religious groups, ethnic groups, LGBTQ+ communities) using the same generation pipeline and human demonstrations adapted for each group. The system ensures comparable coverage and toxicity patterns across groups, enabling evaluation of classifier fairness and bias across different demographic targets.","intents":["Create balanced datasets that cover multiple minority groups with comparable toxicity patterns","Evaluate whether toxicity classifiers have different performance across different target groups","Study how toxic language patterns vary across different demographic targets","Benchmark classifier fairness by comparing false positive/negative rates across groups"],"best_for":["fairness researchers evaluating classifier bias across demographic groups","content moderation teams ensuring balanced protection across different communities","researchers studying how toxicity manifests differently for different target groups"],"limitations":["Requires human demonstrations for each of the 13 groups; scaling to new groups requires new seed data","Group definitions are fixed; cannot easily adapt to new or overlapping demographic categories","Generated datasets may not reflect actual toxicity distribution in real-world content","Balancing toxicity across groups is challenging; some groups may naturally have more/less toxic language patterns"],"requires":["Python 3.8+","Human demonstrations for each of 13 target groups","ALICE framework and generation pipeline","Pre-trained toxicity classifier"],"input_types":["group identifiers (13 supported minority groups)","human demonstrations per group","generation parameters (number of examples per group)"],"output_types":["generated datasets per group (plain text or structured format)","group-level statistics (toxicity distribution, example count)","cross-group comparison metrics (fairness analysis)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_7","uri":"capability://data.processing.analysis.human.annotation.and.quality.control.for.demonstrations","name":"human-annotation-and-quality-control-for-demonstrations","description":"Provides infrastructure for human annotators to create and validate toxic demonstrations that serve as seeds for the generation pipeline. The system includes annotation guidelines, quality control mechanisms, and storage in the demonstrations/ directory with one statement per line, enabling consistent, high-quality seed data that propagates through the entire generation process.","intents":["Create high-quality human demonstrations that accurately represent toxic language patterns","Validate generated examples against human judgment to ensure quality and appropriateness","Maintain consistent annotation standards across multiple annotators and groups","Track demonstration provenance and quality metrics for transparency"],"best_for":["dataset creators building seed data for adversarial generation","teams managing human annotation workflows for sensitive content","researchers studying toxic language patterns through human examples"],"limitations":["Human annotation is time-consuming and expensive; scaling to many groups requires significant resources","Annotator agreement on toxicity is often low; subjective judgments may vary across annotators","Annotators may be exposed to offensive content; requires careful ethical review and support","No automated quality control; requires manual review of all demonstrations"],"requires":["Human annotators with training on toxicity definitions and guidelines","Annotation platform or tool for collecting demonstrations","Quality control process (inter-annotator agreement, review)","Storage system for demonstrations (plain text files in demonstrations/ directory)"],"input_types":["annotation guidelines and toxicity definitions","target groups for annotation","annotator feedback and quality metrics"],"output_types":["human-written toxic demonstrations (plain text, one per line)","annotation metadata (annotator ID, timestamp, quality score)","inter-annotator agreement metrics"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_8","uri":"capability://text.generation.language.benign.statement.generation.for.negative.examples","name":"benign-statement-generation-for-negative-examples","description":"Generates benign (non-toxic) statements about the same minority groups using the same generation pipeline and prompts, creating negative examples for training balanced toxicity classifiers. The system uses the language model to generate innocuous statements that are topically relevant to each group, enabling creation of datasets with balanced toxic/benign ratios.","intents":["Create negative examples for training toxicity classifiers with balanced class distributions","Ensure that classifiers learn to distinguish toxic from benign statements about the same groups","Generate benign statements that are topically relevant to each minority group","Evaluate classifier false positive rates on benign content"],"best_for":["ML practitioners training balanced toxicity classifiers","researchers evaluating classifier specificity (true negative rate)","teams building classifiers that avoid false positives on benign content"],"limitations":["Benign statement generation is less constrained than toxic generation; quality and relevance may vary","No automated validation that generated statements are truly benign; requires human review","Benign statements may not be representative of actual non-toxic content about these groups","Balancing toxic/benign ratio requires careful tuning of generation parameters"],"requires":["Python 3.8+","GPT-3 API access for generation","Prompts designed to generate benign statements (separate from toxic prompts)","Optional: human review to validate benignness"],"input_types":["target groups","prompts for benign statement generation","desired number of benign examples per group"],"output_types":["generated benign statements (plain text)","benign/toxic ratio statistics","validation metadata (human review status)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__cap_9","uri":"capability://automation.workflow.configurable.generation.parameters.and.hyperparameter.tuning","name":"configurable-generation-parameters-and-hyperparameter-tuning","description":"Exposes configurable parameters for controlling the generation process, including beam width, scoring weights (fluency vs. adversarial), maximum sequence length, number of examples per group, and demonstration selection strategy. The system enables researchers to tune these hyperparameters to control the quality, diversity, and adversarial strength of generated datasets without modifying core code.","intents":["Control the balance between fluency and adversarial objectives during generation","Tune beam search parameters to trade off quality and computational cost","Adjust generation parameters to produce datasets with specific properties (length, diversity, toxicity level)","Experiment with different generation strategies without code changes"],"best_for":["researchers experimenting with different generation strategies","teams tuning generation parameters for specific use cases","practitioners optimizing computational cost vs. quality tradeoffs"],"limitations":["No automated hyperparameter optimization; manual tuning required","Parameter interactions are complex; changing one parameter may have unexpected effects on output quality","No guidance on optimal parameter values; requires experimentation and domain knowledge","Configuration files may be error-prone; no validation of parameter ranges"],"requires":["Python 3.8+","Configuration file or command-line arguments for parameter specification","Understanding of parameter semantics and interactions"],"input_types":["beam width (integer)","scoring weights (fluency vs. adversarial, floats)","maximum sequence length (integer)","number of examples per group (integer)","demonstration selection strategy (string)"],"output_types":["generated datasets with specified parameters","generation logs with parameter values and statistics"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"toxigen__headline","uri":"capability://model.training.dataset.for.training.toxicity.detection.models","name":"dataset for training toxicity detection models","description":"A large-scale machine-generated dataset designed to train and evaluate classifiers that detect subtle and implicit forms of toxicity in text related to minority groups.","intents":["best dataset for toxicity detection","dataset for training classifiers on toxic text","machine-generated dataset for evaluating toxicity","toxic statement dataset for AI models","best resources for training on implicit toxicity"],"best_for":["researchers in NLP","developers building toxicity classifiers"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["model-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"low","permissions":["Python 3.8+","PyTorch 1.10.2+","Transformers 4.12.3+","OpenAI API key for GPT-3 access","Pre-trained toxicity classifier (HateBERT or RoBERTa) loaded locally or via Hugging Face","Human-written demonstrations stored in demonstrations/ directory (plain text format)","Configurable prompt template (provided in make_prompts.py)","Generated adversarial dataset with labels","Pre-trained classifier for evaluation","Evaluation script or library (scikit-learn, etc.)"],"failure_modes":["Requires OpenAI API access and associated costs for GPT-3 inference during generation","Beam search algorithm adds computational overhead; generation time scales with beam width and sequence length","Generated examples may contain offensive content by design; requires careful ethical review before deployment","Classifier integration limited to HateBERT/RoBERTa; extending to other classifiers requires custom scoring implementations","Requires high-quality human demonstrations as seeds; poor seed examples propagate through generated dataset","Generation quality depends on GPT-3 prompt engineering; template changes may significantly alter output distribution","Limited to 13 pre-defined minority groups; extending to new groups requires new human demonstrations","No built-in deduplication; generated examples may contain near-duplicates across groups","Metrics assume binary toxic/benign labels; no support for multi-class toxicity levels","Adversarial success rate depends on classifier threshold; different thresholds may give different results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.297Z","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=toxigen","compare_url":"https://unfragile.ai/compare?artifact=toxigen"}},"signature":"rieskPGPFCSuGFlVBW0uyZx2X6nNiy9NDJvRASXRzZXc/vCbEZYnJUPpb7PkanY+2D8nLaXQd/pRBQ2N8RWqCg==","signedAt":"2026-06-21T09:13:19.890Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/toxigen","artifact":"https://unfragile.ai/toxigen","verify":"https://unfragile.ai/api/v1/verify?slug=toxigen","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"}}