Response Structure

Overview

Guardian API returns structured JSON responses with comprehensive moderation data from all four models.

Response Schema

{
  "text": string,           // Original or sanitized input
  "label": {                // Individual model outputs
    "sexism": {...},
    "toxicity": {...},
    "rules": {...}
  },
  "ensemble": {...},        // Combined decision
  "meta": {...}             // Request metadata
}

Top-Level Fields

`text` (string)

The input text that was moderated. May be truncated if very long.

{
  "text": "Your input text here"
}

`label` (object)

Outputs from all three detection models.

`ensemble` (object)

Final moderation decision combining all models.

`meta` (object)

Request metadata including processing time and models used.

Label Structure

Sexism Label

{
  "label": {
    "sexism": {
      "score": 0.724,                    // Confidence score (0-1)
      "severity": "high",                // "low", "moderate", or "high"
      "model_version": "sexism_lasso_v1",  // Model identifier
      "threshold_met": true              // Whether score exceeds threshold
    }
  }
}

Field	Type	Description
`score`	float	LASSO model confidence (0.0 to 1.0)
`severity`	string	Severity level based on score
`model_version`	string	Model identifier for tracking
`threshold_met`	boolean	True if score ≥ 0.400

Toxicity Label

{
  "label": {
    "toxicity": {
      "overall": 0.742,                  // Overall toxicity score
      "insult": 0.631,                   // Insult score
      "threat": 0.123,                   // Threat score
      "identity_attack": 0.412,          // Identity attack score
      "profanity": 0.584,                // Profanity score
      "model_version": "toxic_roberta_v1"  // Model identifier
    }
  }
}

Field	Type	Description
`overall`	float	Maximum toxicity across all categories
`insult`	float	Personal insult score (0-1)
`threat`	float	Threatening language score (0-1)
`identity_attack`	float	Identity-based attack score (0-1)
`profanity`	float	Profane language score (0-1)
`model_version`	string	Model identifier

The overall score is automatically set to at least the maximum of all sub-category scores.

Rules Label

{
  "label": {
    "rules": {
      "slur_detected": false,            // Slur flag
      "threat_detected": true,           // Threat pattern flag
      "self_harm_flag": false,           // Self-harm phrase flag
      "profanity_flag": true,            // Profanity flag
      "caps_abuse": false,               // Excessive caps flag
      "character_repetition": false,     // Repeated chars flag
      "model_version": "rules_v1"        // Model identifier
    }
  }
}

Field	Type	Description
`slur_detected`	boolean	True if slurs found
`threat_detected`	boolean	True if threat patterns matched
`self_harm_flag`	boolean	True if self-harm phrases found
`profanity_flag`	boolean	True if profanity detected
`caps_abuse`	boolean	True if >70% uppercase
`character_repetition`	boolean	True if 3+ repeated characters
`model_version`	string	Model identifier

Ensemble Structure

The ensemble object provides the final moderation decision:

{
  "ensemble": {
    "summary": "likely_harmful",       // Overall assessment
    "primary_issue": "sexism",         // Main concern
    "score": 0.612,                    // Combined score (0-1)
    "severity": "high"                 // "low", "moderate", or "high"
  }
}

Summary Values

Value	Score Range	Meaning
`likely_safe`	0.0 - 0.1	No significant harmful content
`potentially_harmful`	0.1 - 0.3	Some harmful indicators present
`likely_harmful`	0.3 - 0.6	Probable harmful content
`highly_harmful`	0.6 - 1.0	Strong evidence of harmful content

Primary Issue Values

Value	Description
`"none"`	No significant issues detected
`"sexism"`	Sexist content is the main concern
`"toxicity"`	Toxic language is the main concern
`"slur"`	Slur detected by rules
`"threat"`	Threat detected by rules
`"self_harm"`	Self-harm content detected
`"harmful_content"`	Generic harmful content

Meta Structure

Metadata about the request and processing:

{
  "meta": {
    "processing_time_ms": 24,          // Total processing time
    "models_used": [                   // Models that ran
      "sexism_lasso_v1",
      "toxic_roberta_v1",
      "rules_v1"
    ]
  }
}

Field	Type	Description
`processing_time_ms`	integer	Total time to process request (milliseconds)
`models_used`	array	List of model versions used

Complete Example

Harmful Content
Safe Content
Mixed Signals

Request:

{
  "text": "Women belong in the kitchen"
}

Response:

{
  "text": "Women belong in the kitchen",
  "label": {
    "sexism": {
      "score": 0.847,
      "severity": "high",
      "model_version": "sexism_lasso_v1",
      "threshold_met": true
    },
    "toxicity": {
      "overall": 0.621,
      "insult": 0.543,
      "threat": 0.087,
      "identity_attack": 0.621,
      "profanity": 0.124,
      "model_version": "toxic_roberta_v1"
    },
    "rules": {
      "slur_detected": false,
      "threat_detected": false,
      "self_harm_flag": false,
      "profanity_flag": false,
      "caps_abuse": false,
      "character_repetition": false,
      "model_version": "rules_v1"
    }
  },
  "ensemble": {
    "summary": "highly_harmful",
    "primary_issue": "sexism",
    "score": 0.689,
    "severity": "high"
  },
  "meta": {
    "processing_time_ms": 27,
    "models_used": [
      "sexism_lasso_v1",
      "toxic_roberta_v1",
      "rules_v1"
    ]
  }
}

Request:

{
  "text": "I love this product! It's amazing!"
}

Response:

{
  "text": "I love this product! It's amazing!",
  "label": {
    "sexism": {
      "score": 0.043,
      "severity": "low",
      "model_version": "sexism_lasso_v1",
      "threshold_met": false
    },
    "toxicity": {
      "overall": 0.021,
      "insult": 0.012,
      "threat": 0.008,
      "identity_attack": 0.010,
      "profanity": 0.015,
      "model_version": "toxic_roberta_v1"
    },
    "rules": {
      "slur_detected": false,
      "threat_detected": false,
      "self_harm_flag": false,
      "profanity_flag": false,
      "caps_abuse": false,
      "character_repetition": false,
      "model_version": "rules_v1"
    }
  },
  "ensemble": {
    "summary": "likely_safe",
    "primary_issue": "none",
    "score": 0.024,
    "severity": "low"
  },
  "meta": {
    "processing_time_ms": 19,
    "models_used": [
      "sexism_lasso_v1",
      "toxic_roberta_v1",
      "rules_v1"
    ]
  }
}

Request:

{
  "text": "This is garbage but I'll try again"
}

Response:

{
  "text": "This is garbage but I'll try again",
  "label": {
    "sexism": {
      "score": 0.123,
      "severity": "low",
      "model_version": "sexism_lasso_v1",
      "threshold_met": false
    },
    "toxicity": {
      "overall": 0.412,
      "insult": 0.234,
      "threat": 0.067,
      "identity_attack": 0.089,
      "profanity": 0.412,
      "model_version": "toxic_roberta_v1"
    },
    "rules": {
      "slur_detected": false,
      "threat_detected": false,
      "self_harm_flag": false,
      "profanity_flag": true,
      "caps_abuse": false,
      "character_repetition": false,
      "model_version": "rules_v1"
    }
  },
  "ensemble": {
    "summary": "likely_harmful",
    "primary_issue": "toxicity",
    "score": 0.341,
    "severity": "moderate"
  },
  "meta": {
    "processing_time_ms": 22,
    "models_used": [
      "sexism_lasso_v1",
      "toxic_roberta_v1",
      "rules_v1"
    ]
  }
}

Using Response Data

Decision Making

Check ensemble.summary

Use the summary for quick decisions:

"likely_safe": Allow content
"potentially_harmful": Flag for review
"likely_harmful" or "highly_harmful": Block or moderate

Check ensemble.primary_issue

Understand why content was flagged:

Show specific feedback to users
Route to appropriate moderators
Apply category-specific rules

Use individual labels for detail

Access specific model outputs for:

Detailed reporting
Custom threshold logic
Audit trails

Monitor processing_time_ms

Track performance:

Identify slow requests
Optimize infrastructure
Set appropriate timeouts

Example Implementation

def handle_moderation_result(response):
    ensemble = response["ensemble"]

    if ensemble["summary"] == "highly_harmful":
        # Block immediately
        return "BLOCK"

    elif ensemble["summary"] == "likely_harmful":
        # Check specific issues
        if ensemble["primary_issue"] in ["threat", "self_harm"]:
            return "BLOCK_AND_ALERT"
        else:
            return "HOLD_FOR_REVIEW"

    elif ensemble["summary"] == "potentially_harmful":
        # Allow with monitoring
        return "ALLOW_WITH_FLAG"

    else:
        # Safe to publish
        return "ALLOW"

Next Steps

API Reference

Try the moderation endpoint

Ensemble

Learn how scores are calculated

Python SDK

Use the Python SDK

JavaScript SDK

Use the JavaScript SDK

Getting Started

Core Concepts

SDKs

Configuration

Use Cases

About

Overview

Response Schema

Top-Level Fields

`text` (string)

`label` (object)

`ensemble` (object)

`meta` (object)

Label Structure

Sexism Label

Toxicity Label

Rules Label

Ensemble Structure

Summary Values

Primary Issue Values

Meta Structure

Complete Example

Using Response Data

Decision Making

Example Implementation

Next Steps

API Reference

Ensemble

Python SDK

JavaScript SDK

​Overview

​Response Schema

​Top-Level Fields

​text (string)

​label (object)

​ensemble (object)

​meta (object)

​Label Structure

​Sexism Label

​Toxicity Label

​Rules Label

​Ensemble Structure

​Summary Values

​Primary Issue Values

​Meta Structure

​Complete Example

​Using Response Data

​Decision Making

​Example Implementation

​Next Steps

API Reference

Ensemble

Python SDK

JavaScript SDK

Overview

Response Schema

Top-Level Fields

`text` (string)

`label` (object)

`ensemble` (object)

`meta` (object)

Label Structure

Sexism Label

Toxicity Label

Rules Label

Ensemble Structure

Summary Values

Primary Issue Values

Meta Structure

Complete Example

Using Response Data

Decision Making

Example Implementation

Next Steps