File size: 3,160 Bytes
9ec538b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | """Evaluate sentence segmentation (sent_tokenize) against test cases."""
import json
import argparse
from underthesea import sent_tokenize as underthesea_sent_tokenize
def evaluate(test_cases_path: str, verbose: bool = False, improved: bool = False):
if improved:
from sent_tokenize import sent_tokenize
else:
sent_tokenize = underthesea_sent_tokenize
with open(test_cases_path, "r", encoding="utf-8") as f:
test_cases = json.load(f)
total = len(test_cases)
correct = 0
incorrect = 0
by_category = {}
failures = []
for tc in test_cases:
input_text = tc["input"]
expected = tc["expected"]
category = tc["category"]
actual = sent_tokenize(input_text)
is_correct = actual == expected
if category not in by_category:
by_category[category] = {"total": 0, "correct": 0}
by_category[category]["total"] += 1
if is_correct:
correct += 1
by_category[category]["correct"] += 1
else:
incorrect += 1
failures.append(
{
"id": tc["id"],
"category": category,
"input": input_text,
"expected": expected,
"actual": actual,
}
)
# Print report
print("=" * 60)
label = "IMPROVED (trained Punkt)" if improved else "BASELINE (underthesea)"
print(f"SENTENCE SEGMENTATION EVALUATION - {label}")
print("=" * 60)
print(f"\nTotal: {total} Correct: {correct} Incorrect: {incorrect}")
print(f"Accuracy: {100 * correct / total:.1f}%")
print()
print(f"{'Category':<25} {'Total':>6} {'Correct':>8} {'Acc':>7}")
print("-" * 48)
for cat in sorted(by_category):
stats = by_category[cat]
acc = 100 * stats["correct"] / stats["total"]
print(f"{cat:<25} {stats['total']:>6} {stats['correct']:>8} {acc:>6.1f}%")
if verbose and failures:
print(f"\n{'='*60}")
print(f"FAILURES ({len(failures)})")
print("=" * 60)
for f in failures:
print(f"\n[{f['id']}] {f['category']}")
print(f" Input: {f['input'][:100]}...")
print(f" Expected: {[s[:60] for s in f['expected']]}")
print(f" Actual: {[s[:60] for s in f['actual']]}")
return {
"total": total,
"correct": correct,
"incorrect": incorrect,
"accuracy": correct / total,
"by_category": by_category,
"failures": failures,
}
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--test-cases",
default="test_cases.json",
help="Path to test cases JSON file",
)
parser.add_argument(
"-v", "--verbose", action="store_true", help="Show failure details"
)
parser.add_argument(
"--improved",
action="store_true",
help="Use trained Punkt model instead of underthesea default",
)
args = parser.parse_args()
evaluate(args.test_cases, verbose=args.verbose, improved=args.improved)
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