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Make comparing hashes more human friendly by using verb-adj-noun format.

Project description

WordHasher

Hashes are cool. But gosh they are ugly to read...

Let's convert them to verb-noun-adjective form to be more human friendly!

We are going to use WordNet to get some words and hashlib to get some hashes.

Example

>>> from wordhasher import WordHasher
>>> wh = WordHasher()
>>> print(wh)
WordHasher:
     nouns: 9698
adjectives: 3644
     verbs: 2872
>>> wh.from_str('This is a test.') 
catnap-abatic-upshot
>>> wh.from_str(__file__)
syphon-abashed-decidua
>>> wh.sample()
keep-vain-smugness-247
>>> wh.sample(mode="an")
inviting-patrial
>>> wh.sample(mode="anN")
unsaved-asshole-908

Credits

Princeton University "About WordNet." WordNet. Princeton University. 2010.

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  • Download URL: wordhasher-0.2.0.tar.gz
  • Upload date:
  • Size: 48.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.0

File hashes

Hashes for wordhasher-0.2.0.tar.gz
Algorithm Hash digest
SHA256 90037c7b32c5f2d5b2995718229803ac8410c296f59a9a7c2820938ffe1bb57b
MD5 6290974cc27a104c45e71c68f1e188fa
BLAKE2b-256 ac84c2a4745652ef86be90bc9f682805923d272c163ab2c3a55dc6f102be9bab

See more details on using hashes here.

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Details for the file wordhasher-0.2.0-py3-none-any.whl.

File metadata

  • Download URL: wordhasher-0.2.0-py3-none-any.whl
  • Upload date:
  • Size: 47.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.0

File hashes

Hashes for wordhasher-0.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 dc247aed228c6853f8033f882a104185c32cb91cef303ba301e3e056de273270
MD5 2818c93cb4def3621601b9f5bbe9a839
BLAKE2b-256 fc7888951786cc2188b69022174669aced1f7348e76722bd230401b927ca5172

See more details on using hashes here.

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