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Measures the served heads on a curated set and reports inference latency.
The evaluation set is grouped by the linguistic phenomenon each case probes:
negation, mixed sentiment and sarcasm are where a sentence-level classifier
tends to break. The irony head is reported on the same cases, which shows
whether it fires on the group the sentiment head fails and stays quiet on the
rest. This set is small and hand-written; the held-out figures for the irony
head come from the TweetEval test split instead.
Usage: python evaluate.py
"""
import json
import statistics
import time
from collections import defaultdict
from pathlib import Path
from core.config import MODEL_NAME
from core.service import analyze_text
EVAL_SET = Path(__file__).resolve().parent / "data" / "eval_set.json"
def percentile(values: list, fraction: float) -> float:
"""Nearest-rank percentile; the sample is far too small for interpolation."""
ordered = sorted(values)
index = min(int(len(ordered) * fraction), len(ordered) - 1)
return ordered[index]
def main() -> None:
cases = json.loads(EVAL_SET.read_text(encoding="utf-8"))
print(f"Model: {MODEL_NAME}")
print(f"Cases: {len(cases)}\n")
print("Warming up...")
analyze_text("warm up")
latencies = []
by_category = defaultdict(lambda: {"total": 0, "correct": 0, "ironic": 0})
failures = []
for case in cases:
started = time.perf_counter()
result = analyze_text(case["text"])
latencies.append((time.perf_counter() - started) * 1000)
correct = result["label"] == case["expected"]
bucket = by_category[case["category"]]
bucket["total"] += 1
bucket["correct"] += int(correct)
bucket["ironic"] += int(result["irony"]["detected"])
if not correct:
failures.append((case, result))
total = len(cases)
correct = sum(b["correct"] for b in by_category.values())
print(f"\nAccuracy: {correct}/{total} = {correct / total:.1%}\n")
header = f"{'Category':<18}{'Correct':>9}{'Total':>7}{'Accuracy':>11}{'Flagged ironic':>16}"
print(header)
print("-" * len(header))
for category in sorted(by_category):
bucket = by_category[category]
flagged = f"{bucket['ironic']}/{bucket['total']}"
print(
f"{category:<18}{bucket['correct']:>9}{bucket['total']:>7}"
f"{bucket['correct'] / bucket['total']:>10.0%}{flagged:>16}"
)
print(f"\nLatency over {len(latencies)} calls (ms, CPU, batch of one):")
print(f" mean {statistics.mean(latencies):.1f}")
print(f" p50 {percentile(latencies, 0.50):.1f}")
print(f" p95 {percentile(latencies, 0.95):.1f}")
print(f" max {max(latencies):.1f}")
if failures:
print(f"\nMisclassified ({len(failures)}):")
for case, result in failures:
print(f" [{case['category']}] expected {case['expected']}, "
f"got {result['label']} ({result['score']:.2f}), "
f"irony {result['irony']['score']:.2f}")
print(f" {case['text']}")
if __name__ == "__main__":
main()
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