bleuscore

A fast bleu score calculator


Keywords
NLP, Tokenizer, BLEU, DeepLearning, bleu-score, deep-learning, maturin, ngrams, pyo3, python, rust
License
MIT
Install
pip install bleuscore==0.1.1

Documentation

bleuscore

codecov MIT licensed Crates.io PyPI - Version docs.rs

bleuscore is a fast BLEU score calculator written in rust.

Installation

The python package has been published to pypi, so we can install it directly with many ways:

  • pip

    pip install bleuscore
  • poetry

    poetry add bleuscore
  • uv

    uv pip install bleuscore

Quick Start

The usage is exactly same with huggingface evaluate:

- import evaluate
+ import bleuscore

predictions = ["hello there general kenobi", "foo bar foobar"]
references = [
    ["hello there general kenobi", "hello there !"],
    ["foo bar foobar"]
]

- bleu = evaluate.load("bleu")
- results = bleu.compute(predictions=predictions, references=references)
+ results = bleuscore.compute(predictions=predictions, references=references)

print(results)
# {'bleu': 1.0, 'precisions': [1.0, 1.0, 1.0, 1.0], 'brevity_penalty': 1.0, 
# 'length_ratio': 1.1666666666666667, 'translation_length': 7, 'reference_length': 6}

Benchmark

TLDR: We got more than 10x speedup when the corpus size beyond 100K

Benchmark

We use the demo data shown in quick start to do this simple benchmark. You can check the benchmark/simple for the benchmark source code.

  • rs_bleuscore: bleuscore python library
  • local_hf_bleu: huggingface evaluate bleu algorithm in local
  • sacre_bleu: sacrebleu
    • Note that we got different result with sacrebleu in the simple demo data and all the rests have same result
  • hf_evaluate: huggingface evaluate bleu algorithm with evaluate package

The N is used to enlarge the predictions/references size by simply duplication the demo data as shown before. We can see that as N increase, the bleuscore gets better performance. You can navigate benchmark for more benchmark details.

N=100

hyhyperfine --warmup 5 --runs 10   \
  "python simple/rs_bleuscore.py 100" \
  "python simple/local_hf_bleu.py 100" \
  "python simple/sacre_bleu.py 100"   \
  "python simple/hf_evaluate.py 100"

Benchmark 1: python simple/rs_bleuscore.py 100
  Time (mean ± σ):      19.0 ms ±   2.6 ms    [User: 17.8 ms, System: 5.3 ms]
  Range (min … max):    14.8 ms …  23.2 ms    10 runs

Benchmark 2: python simple/local_hf_bleu.py 100
  Time (mean ± σ):      21.5 ms ±   2.2 ms    [User: 19.0 ms, System: 2.5 ms]
  Range (min … max):    16.8 ms …  24.1 ms    10 runs

Benchmark 3: python simple/sacre_bleu.py 100
  Time (mean ± σ):      45.9 ms ±   2.2 ms    [User: 38.7 ms, System: 7.1 ms]
  Range (min … max):    43.5 ms …  50.9 ms    10 runs

Benchmark 4: python simple/hf_evaluate.py 100
  Time (mean ± σ):      4.504 s ±  0.429 s    [User: 0.762 s, System: 0.823 s]
  Range (min … max):    4.163 s …  5.446 s    10 runs

Summary
  python simple/rs_bleuscore.py 100 ran
    1.13 ± 0.20 times faster than python simple/local_hf_bleu.py 100
    2.42 ± 0.35 times faster than python simple/sacre_bleu.py 100
  237.68 ± 39.88 times faster than python simple/hf_evaluate.py 100

N = 1K ~ 1M

Command Mean [ms] Min [ms] Max [ms] Relative
python simple/rs_bleuscore.py 1000 20.3 ± 1.3 18.2 21.4 1.00
python simple/local_hf_bleu.py 1000 45.8 ± 1.2 44.2 47.5 2.26 ± 0.16
python simple/rs_bleuscore.py 10000 37.8 ± 1.5 35.9 39.5 1.87 ± 0.14
python simple/local_hf_bleu.py 10000 295.0 ± 5.9 288.6 304.2 14.55 ± 0.98
python simple/rs_bleuscore.py 100000 219.6 ± 3.3 215.3 224.0 10.83 ± 0.72
python simple/local_hf_bleu.py 100000 2781.4 ± 42.2 2723.1 2833.0 137.13 ± 9.10
python simple/rs_bleuscore.py 1000000 2048.8 ± 31.4 2013.2 2090.3 101.01 ± 6.71
python simple/local_hf_bleu.py 1000000 28285.3 ± 100.9 28182.1 28396.1 1394.51 ± 90.21