| """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("=" * 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) |
|
|
