So you want to know how yasbd stacks up against the competition? Fair enough.

EN Golden benchmark

Aggregate score across all 92 English edge cases in EN_GOLDEN_DATA.py via run_golden.py. Expanded from pysbd's original 48 to 92 cases: we removed biased/wrong expectations (like splitting mid-ellipsis or bad punctuation in dialog) and added cases for abbreviation chains, contiguous terminators, exclamation-safe words, academic citations, and more.

The Passed column is a strict sentence-string equality check. Alongside it, we report boundary-level Precision, Recall, and F1 computed by scorer.py.

Boundary metric methodology

evaluate_segmentation scores against the positions where sentences end, not the sentence strings themselves, so a boundary that is merely shifted (e.g. trailing whitespace) still counts toward accuracy. Implementation:

1. Word-level binary arrays. Every sentence is split into words. The final word of each sentence is marked 1 (a sentence boundary); all other words are marked 0.
2. Alignment. If the model dropped or added words, the two arrays differ in length. The shorter array is right-justified with 0 padding so both arrays share the final word's boundary marker.
3. Confusion counts. Each aligned word position is a True Positive (1/1), False Positive (0/1), or False Negative (1/0).
4. Averages. TP/FP/FN are summed across all 92 cases, then Precision = TP ÷ (TP+FP), Recall = TP ÷ (TP+FN), and F1 = the harmonic mean of the two.

All three are bounded [0, 1]; they fall back to 0.0 when no useful boundary signal exists (e.g. an empty gold).

yasbd achieves 91/92 (98.9%). The only failing case is the Ave. abbreviation followed by a capitalized new sentence — a known limitation of rule-based abbreviation suppression.

Book benchmarks

Real-world performance on full-length books via bench_books.py.

Alice in Wonderland (148,208 chars)

Adventures of Sherlock Holmes (593,911 chars)