LLM Evaluation by @wshobson

LLM Evaluation by @wshobson · Skillet

from dataclasses import dataclass
from typing import Callable
import numpy as np

@dataclass
class Metric:
    name: str
    fn: Callable

    @staticmethod
    def accuracy():
        return Metric("accuracy", calculate_accuracy)

    @staticmethod
    def bleu():
        return Metric("bleu", calculate_bleu)

    @staticmethod
    def bertscore():
        return Metric("bertscore", calculate_bertscore)

    @staticmethod
    def custom(name: str, fn: Callable):
        return Metric(name, fn)

class EvaluationSuite:
    def __init__(self, metrics: list[Metric]):
        self.metrics = metrics

    async def evaluate(self, model, test_cases: list[dict]) -> dict:
        results = {m.name: [] for m in self.metrics}

        for test in test_cases:
            prediction = await model.predict(test["input"])

            for metric in self.metrics:
                score = metric.fn(
                    prediction=prediction,
                    reference=test.get("expected"),
                    context=test.get("context")
                )
                results[metric.name].append(score)

        return {
            "metrics": {k: np.mean(v) for k, v in results.items()},
            "raw_scores": results
        }

# Usage
suite = EvaluationSuite([
    Metric.accuracy(),
    Metric.bleu(),
    Metric.bertscore(),
    Metric.custom("groundedness", check_groundedness)
])

test_cases = [
    {
        "input": "What is the capital of France?",
        "expected": "Paris",
        "context": "France is a country in Europe. Paris is its capital."
    },
]

results = await suite.evaluate(model=your_model, test_cases=test_cases)

LLM Evaluation

LLM Evaluation

When to Use This Skill

Core Evaluation Types

1. Automated Metrics

2. Human Evaluation

3. LLM-as-Judge

Quick Start

Detailed patterns and worked examples