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API Reference

Output Format

Moderators provides normalized, consistent output regardless of the underlying model or framework.

Python API

Results are returned as a list of PredictionResult dataclass instances:

[
  PredictionResult(
    source_path='',
    classifications={'porn': 0.9821},
    detections=[],
    raw_output={'label': 'porn', 'score': 0.9821}
  ),
  ...
]

JSON Format (CLI)

The CLI outputs the same structure as JSON:

[
    {
        "source_path": "",
        "classifications": { "porn": 0.9821 },
        "detections": [],
        "raw_output": { "label": "porn", "score": 0.9821 }
    }
]

Converting Python Results to JSON

Use dataclasses.asdict() to convert Python results to JSON-ready dictionaries:

from dataclasses import asdict
from moderators import AutoModerator

moderator = AutoModerator.from_pretrained("viddexa/nsfw-detection-2-mini")
result = moderator("/path/to/image.jpg")
json_ready = [asdict(r) for r in result]
print(json_ready)

PredictionResult Fields

  • source_path (str): Path to the input file, or empty string for text/direct input
  • classifications (dict): Normalized classification results as {label: score} pairs
  • detections (list): Object detection results (empty for classification tasks)
  • raw_output (dict): Original model output for reference

AutoModerator API

Loading Models

From Hugging Face Hub:

from moderators import AutoModerator

moderator = AutoModerator.from_pretrained("viddexa/nsfw-detection-2-mini")

From local directory:

moderator = AutoModerator.from_pretrained("/path/to/model")

With offline mode:

moderator = AutoModerator.from_pretrained("model-id", local_files_only=True)

Running Inference

Image input:

result = moderator("/path/to/image.jpg")

Text input:

result = moderator("Text to classify")

Task Detection

Moderators automatically detects the task type from the model's config.json when possible, so you don't need to specify the task manually.

Supported tasks:

  • Image classification (e.g., NSFW detection)
  • Text classification (e.g., sentiment analysis, toxicity detection)

Model Selection

  • From the Hub: Pass a model ID like viddexa/nsfw-detection-2-mini or any compatible Transformers model
  • From disk: Pass a local folder that contains a config.json next to your model weights

The system automatically infers the task and integration from the config when possible.