Detect Hate Speech in User-Generated Content
Promptfoo example workflow for evaluating hate speech detection models using Hugging Face API with configurable test cases and result visualization.
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Why it matters
Automatically identify and flag hate speech in text content to maintain safe online communities and comply with content moderation policies. This asset helps teams screen user-generated content at scale using Hugging Face's classification models.
Outcomes
What it gets done
Classify text inputs as hate speech or acceptable content
Integrate Hugging Face API for real-time content analysis
Evaluate detection accuracy across test cases
Generate structured reports on flagged content
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/pfoo-hate-speech-detection | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
Reports
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Steps
Steps in the chain
Overview
Hate Speech Detection
A promptfoo example workflow that evaluates hate speech detection models from Hugging Face. It provides a configurable testing framework with predefined test cases and visualization tools to compare model performance. Use this when you need to benchmark hate speech detection models before production deployment or compare multiple Hugging Face models for content moderation. It helps establish systematic testing for text classification systems.
What it does
This promptfoo example demonstrates how to evaluate hate speech detection models from Hugging Face. It provides a workflow that you can configure to test models and view results through promptfoo's evaluation interface.
When to use - and when NOT to
Use this example when you need to benchmark hate speech detection models before production deployment, compare multiple Hugging Face models for content moderation tasks, or establish a testing framework for text classification systems. Do NOT use this as a production-ready hate speech filter - it is an evaluation example designed to help you test and compare models, not a complete moderation solution.
Inputs and outputs
You provide your HF_API_TOKEN environment variable and edit the promptfooconfig.yaml file to configure which models and test cases to evaluate. The workflow outputs evaluation results that you can view through the promptfoo interface.
Integrations
This example integrates with Hugging Face's API for accessing hate speech detection models. It uses promptfoo's evaluation framework for running tests and visualizing results.
Who it's for
This example serves ML engineers and content moderation teams who need to evaluate hate speech detection models before deployment.
Getting started
Initialize the example with:
npx promptfoo@latest init --example huggingface/hate-speech-detection
cd huggingface/hate-speech-detection
Set your HF_API_TOKEN environment variable, edit promptfooconfig.yaml to configure your test cases, then run:
promptfoo eval
Afterwards, view results by running promptfoo view.
Source README
huggingface/hate-speech-detection (Huggingface Hate Speech Detection)
You can run this example with:
npx promptfoo@latest init --example huggingface/hate-speech-detection
cd huggingface/hate-speech-detection
Usage
To get started, set your HF_API_TOKEN environment variable.
Next, edit promptfooconfig.yaml.
Then run:
promptfoo eval
Afterwards, you can view the results by running promptfoo view
FAQ
Common questions
Discussion
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