juryeval 0.5.0

Lightweight NLP/LLM evaluation toolkit — metrics, judges, significance testing

juryeval

Lightweight NLP/LLM evaluation toolkit: metrics, LLM-as-Judge, statistical significance testing, prompt robustness analysis, and a CLI.

Designed for fast smoke-tests, demos, and as a drop-in dependency for frameworks like LM Eval Harness, DeepEval, Lighteval, and LangChain.

Install

pip install juryeval

Optional extras:

Extra	What you get
[judge]	LLM-as-Judge (openai)
[semantic]	Embedding similarity (sentence-transformers)
[lmeval]	lm-eval-harness integration
[full]	All metrics (sklearn, sacrebleu, transformers, torch, etc.)
[all]	Everything

CLI

# Score a single output
juryeval score --question "What is 2+2?" --output "4"

# Compare two outputs
juryeval compare --question "Capital of France?" --output-a "Paris" --output-b "London"

# Evaluate a dataset
juryeval evaluate --metric classification --predictions preds.json --references refs.json

# Judge calibration
juryeval calibrate --model gpt-4

# Prompt sensitivity analysis
juryeval prompt --question "Explain AI" --num-variants 5

Input files are JSON arrays, JSONL, or plain text (one sample per line). Run juryeval <command> --help for full options.

Python API

Metrics

from juryeval import (
    eval_classification, eval_translation, eval_summarization,
    perplexity, flesch_kincaid, bert_score,
)

acc_f1 = eval_classification(preds=["pos", "neg"], refs=["pos", "pos"])
bleu   = eval_translation(preds=["hello world"], refs=["bonjour le monde"])
rouge  = eval_summarization(preds=["summary"], refs=["reference"])
ppl    = perplexity("This is a sentence.")
fk     = flesch_kincaid("This is easy to read.")
bs     = bert_score(preds=["answer"], refs=["reference"])

LLM-as-Judge

from juryeval import PairwiseJudge, PointwiseJudge, MultiJudgeEnsemble, JudgeCalibration

# Pairwise comparison
judge = PairwiseJudge("gpt-4")
result = judge.compare(
    answer_a="Paris is the capital of France.",
    answer_b="It's Paris.",
    question="What is the capital of France?",
)
# {"winner": "A", "score": 1.0, "reason": "..."}

# Pointwise scoring
scorer = PointwiseJudge("gpt-4")
result = scorer.score("Paris is the capital.", question="What is the capital of France?")
# {"score": 0.9, "reason": "..."}

# Multi-judge ensemble
ensemble = MultiJudgeEnsemble([
    PairwiseJudge("gpt-4"),
    PairwiseJudge("claude-3-opus"),
])
result = ensemble.compare(answer_a, answer_b, question)
# {"majority_winner": "A", "agreement": 0.67, ...}

# Judge calibration
cal = JudgeCalibration()
report = cal.evaluate(judge)
# {"position_bias": 0.05, "consistency": 0.95, "length_bias": 0.1, ...}

Statistical Significance

from juryeval import bootstrap_ci, compare_models

ci = bootstrap_ci(scores, num_resamples=2000)
# {"estimate": 0.72, "lower": 0.68, "upper": 0.76, "std_err": 0.02}

result = compare_models(model_a_scores, model_b_scores)
# {"win_rate": 0.65, "p_value": 0.003, "mean_a": 0.72, "mean_b": 0.68, ...}

Prompt Robustness

from juryeval import PromptVariance

pv = PromptVariance(model_fn=lambda prompt: "output")
report = pv.analyze("What is 2+2?")
# {"num_variants": 7, "output_length_mean": 5.0, "outputs": [...], ...}

Framework Integrations

Framework	Setup
lm-eval-harness	pip install juryeval[lmeval] then from juryeval.lmeval import register_all; register_all()
DeepEval	pip install deepeval[juryeval] then from deepeval.metrics.juryeval import JuryEvalMetric
Lighteval	pip install lighteval[juryeval] then use JuryEvalPointwiseJudge / JuryEvalPairwiseJudge metrics
LangChain	pip install langchain[juryeval]

See each framework's documentation for detailed usage.

Development

pip install pytest
pytest tests/ -v

License

MIT