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Awesome GitHub RepositoriesCustom Evaluation Metrics
Functions and utilities for defining success criteria to measure model performance.
Distinguishing note: Focuses on user-defined logic for scoring model outputs rather than standard benchmarks.
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Awesome Custom Evaluation Metrics GitHub Repositories
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- stanfordnlp/dspy
stanfordnlp/dspy
32,291View on GitHubDSPy is a declarative programming framework designed for building complex language model applications. It treats model interactions as modular, composable programs, allowing developers to define task logic through typed class schemas rather than relying on manually written prompts. By organizing workflows into hierarchical, reusable Python objects, the framework enables the construction of sophisticated AI systems that manage state and execution flow independently. The framework distinguishes itself through an automated optimization engine that iteratively refines prompt instructions and few-
Enables the creation of custom functions to measure program success and guide optimization.
Python
