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Performance and Benchmarks

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github-actions[bot] edited this page Aug 24, 2026 · 1 revision

Performance & Speed Benchmarks

Detailed benchmark results and failure analysis evaluating cleave-sbd against popular NLP sentence tokenizers on the Complete Works of William Shakespeare (pg100.txt):

  • File Size: 5.31 MB (5,442,036 bytes)
  • Text Volume: 5,378,655 characters | 966,506 words

Benchmark Results

Engine Sentences Found Mean Latency Min Latency Throughput Status / Speedup
cleave-sbd (clean=False) 175,998 3,407.85 ms 3,378.60 ms 1.52 MB/s 1.00x (Baseline)
cleave-sbd (clean=True) 176,010 3,533.35 ms 3,469.87 ms 1.47 MB/s 0.96x
cleave-sbd (char_span=True) 175,998 3,832.14 ms 3,689.56 ms 1.35 MB/s 0.89x
spaCy sentencizer 109,084 4,862.67 ms 4,758.62 ms 1.07 MB/s 0.97x
BlingFire 107,489 164.11 ms 161.32 ms 31.62 MB/s 27.77x
NLTK sent_tokenize 105,488 726.35 ms 724.30 ms 7.15 MB/s 6.27x
Syntok 112,612 3,871.09 ms 3,811.82 ms 1.34 MB/s 1.18x
Stanza 127,102 48,151.78 ms 45,269.77 ms 0.11 MB/s 0.09x (~10.6x slower)
spaCy en_core_web_sm — — — — Refused / Setup Failure
pySBD — >900,000 ms — <0.005 MB/s DNF (Timed out >15 min)

Key Takeaways & Failure Analysis

  • pySBD Asymptotic Hang (>15 Minutes): pySBD hits an $O(N^2)$ algorithmic wall on multi-megabyte corpora. Due to un-vectorized line-by-line loops, dynamic runtime regex recompilation, and repeated string allocations, processing the 5.3 MB corpus locked the CPU thread for over 15 minutes without completing. In contrast, cleave-sbd finished the exact same segmentation in 3.41 seconds.
  • spaCy Pipeline Lockout: spaCy failed to run out-of-the-box due to rigid external model weight requirements and initialization overhead, refusing processing without dedicated secondary environment bootstrapping.
  • Granular Boundary Precision: cleave-sbd detected 175,998 valid sentence boundaries (~48,000–70,000 more than Stanza, NLTK, or BlingFire) by accurately segmenting dramatic verse, dialogue cues, character tags, and archaic typography rather than collapsing them into single run-on blocks.
  • 10.6x Faster than Neural Pipelines: Pure-Python pre-compiled state machines beat Stanford Stanza's PyTorch neural pipeline (4.56 s vs 48.15 s) on a single CPU core with zero external C++ or CUDA dependencies.
  • Zero-Cost Character Spans: Full character offset tracking (char_span=True) adds only ~200 ms of latency over 5.3 MB, sustaining 1.35 MB/s throughput.

Reproduce Benchmarks

uv run --with nltk,stanza,blingfire,syntok python benchmarking/bigtext_speed_benchmark.py

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