Alquimia Fair Forge library - Performance measurement for AI models and assistants
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Project description
Fair Forge
Performance-measurement library for evaluating AI models and assistants
What is Fair Forge?
Fair Forge provides comprehensive metrics for evaluating AI systems:
- Toxicity - Detect toxic language patterns with DIDT fairness framework
- Bias - Analyze biases across protected attributes (gender, race, religion, etc.)
- Context - Assess how well responses align with provided context
- Conversational - Evaluate dialogue quality using Grice's maxims
- Humanity - Measure how natural responses are through emotional analysis
- BestOf - Tournament-style comparison to find the best response
Quick Start
# Install with pip
pip install alquimia-fair-forge
# Or install specific modules
pip install "alquimia-fair-forge[toxicity]"
pip install "alquimia-fair-forge[bias]"
pip install "alquimia-fair-forge[all]"
from fair_forge import Retriever, Dataset
from fair_forge.metrics.toxicity import Toxicity
class MyRetriever(Retriever):
def load_dataset(self) -> list[Dataset]:
# Load your dataset here
pass
metrics = Toxicity.run(MyRetriever, verbose=True)
Documentation
For complete documentation, guides, and API reference visit:
License
MIT License - see LICENSE for details.
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