spaneval 0.2.0

Evaluation library for span-level entity extraction

spaneval

Pure evaluation library for span-level entity extraction — takes ground truth and predicted (entity_type, start, end) tuples and returns precision, recall, and F1. Works with any pipeline that produces character-span predictions: LLM extractors, fine-tuned models, rule-based systems. Configurable overlap strategies, per-type precision/recall targets, and a scalar optimization score for automated prompt engineering.

Inspired by and a generalization of nervaluate.

Installation

pip install spaneval

Relationship to seqeval and nervaluate

seqeval works on IOB/BIO token sequences. LLMs output character spans — converting between the two is lossy and inconvenient.

nervaluate is the closest prior work and covers the SemEval 2013 strategies well. This library extends the same foundation with configurable overlap thresholds, per-type strategy assignment, and score() — a scalar optimization target for automated prompt engineering and hyperparameter search.

Quickstart

from spaneval import evaluate, to_entities

true = to_entities([
    {"entity_type": "PERSON", "start":  0, "end": 10},
    {"entity_type": "ORG",    "start": 34, "end": 44},
])
pred = to_entities([
    {"entity_type": "PERSON", "start":  0, "end":  9},  # slightly off boundary
    {"entity_type": "ORG",    "start": 34, "end": 44},  # exact
])

results = evaluate(true, pred)
results.report()

report() with no arguments shows a ± range across two strategies (Strict / AnyOverlap), giving an instant picture of how boundary precision affects your numbers.

For a guided walkthrough, see the examples:

• examples/quickstart.py — five steps from zero-config to per-type strategy assignment
• examples/goals.py — two steps from goal definition to automated prompt optimization

Strategies

Two branches, covered in full in docs/:

Entity-count strategies score each entity as correct, incorrect, missed, or spurious:

Strategy	Span	Type
Strict	Exact boundaries	Required
Exact	Exact boundaries	Ignored
EntType	Any overlap	Required
Partial	Any overlap; 0.5 credit if boundaries differ	Ignored
ProportionalCoverage	Fraction of true-entity characters covered	Ignored
AnyOverlap	Any overlap = full credit	Ignored
Contains	Prediction must fully contain the true span	Ignored
MinimumOverlap	Configurable threshold and overlap metric	Configurable

TextCoverage counts characters rather than entities — useful when the goal is text redaction rather than entity classification.

Per-type strategies and goals

Different entity types can be evaluated under different strategies and held to different targets:

from spaneval.strategies import Strict, ProportionalCoverage

# different strictness per type
results.report(strategy={"PERSON": Strict(), "DATE": ProportionalCoverage()})

# precision/recall targets per type
from spaneval import Goal
goals = {
    "PERSON": Goal(strategy=Strict(),               recall=0.90, precision=0.80),
    "DATE":   Goal(strategy=ProportionalCoverage(),  recall=0.80, precision=0.70),
}
results.report_goals(goals)