Skip to main content

A fast multithreaded C++ implementation of nltk BLEU.

Project description

FastBLEU Package

This is a fast multithreaded C++ implementation of NLTK BLEU; computing BLEU and SelfBLEU score for a fixed reference set. It can return (Self)BLEU for different (max) n-grams simultaneously and efficiently (e.g. BLEU-2, BLEU-3 and etc.).

Installation

PyPI latest stable release

pip install --user FastBLEU

Sample Usage

Here is an example to compute BLEU-2, BLEU-3, SelfBLEU-2 and SelfBLEU-3:

>>> from fast_bleu import BLEU, SelfBLEU
>>> ref1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',
...          'ensures', 'that', 'the', 'military', 'will', 'forever',
...          'heed', 'Party', 'commands']
>>> ref2 = ['It', 'is', 'the', 'guiding', 'principle', 'which',
...          'guarantees', 'the', 'military', 'forces', 'always',
...          'being', 'under', 'the', 'command', 'of', 'the', 'Party']
>>> ref3 = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',
...          'army', 'always', 'to', 'heed', 'the', 'directions',
...          'of', 'the', 'party']

>>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',
...         'ensures', 'that', 'the', 'military', 'always',
...         'obeys', 'the', 'commands', 'of', 'the', 'party']
>>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',
...         'interested', 'in', 'world', 'history']

>>> list_of_references = [ref1, ref2, ref3]
>>> hypotheses = [hyp1, hyp2]
>>> weights = {'bigram': (1/2., 1/2.), 'trigram': (1/3., 1/3., 1/3.)}

>>> bleu = BLEU(list_of_references, weights)
>>> bleu.get_score(hypotheses)
{'bigram': [0.7453559924999299, 0.0191380231127159], 'trigram': [0.6240726901657495, 0.013720869575946234]}

which means:

  • BLEU-2 for hyp1 is 0.7453559924999299

  • BLEU-2 for hyp2 is 0.0191380231127159

  • BLEU-3 for hyp1 is 0.6240726901657495

  • BLEU-3 for hyp2 is 0.013720869575946234

>>> self_bleu = SelfBLEU(list_of_references, weights)
>>> self_bleu.get_score()
{'bigram': [0.25819888974716115, 0.3615507630310936, 0.37080992435478316],
        'trigram': [0.07808966062765045, 0.20140620205719248, 0.21415334758254043]}

which means:

  • SelfBLEU-2 for ref1 is 0.25819888974716115

  • SelfBLEU-2 for ref2 is 0.3615507630310936

  • SelfBLEU-2 for ref3 is 0.37080992435478316

  • SelfBLEU-3 for ref1 is 0.07808966062765045

  • SelfBLEU-3 for ref2 is 0.20140620205719248

  • SelfBLEU-3 for ref3 is 0.21415334758254043

Caution Each token of reference set is converted to string format during computation.

For further details, refer to the documentation provided in the source codes.

Citation

Please cite our paper if it helps with your research.

@article{https://arxiv.org/abs/1904.03971,
  title={Jointly Measuring Diversity and Quality in Text Generation Models},
  author={Montahaei, Ehsan and Alihosseini, Danial and Baghshah, Mahdieh Soleymani},
  journal={NAACL HLT 2019},
  pages={90},
  year={2019}
}

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for FastBLEU, version 0.0.4
Filename, size File type Python version Upload date Hashes
Filename, size FastBLEU-0.0.4.tar.gz (9.7 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page