Skip to main content

Methods for evaluating low-resource word embedding models trained with gensim

Project description

gensim-evaluations

This library provides methods for evaluating word embedding models loaded with gensim. Currently, it implements two methods designed specifically for the evaluation of low-resource models. The code allows users to automatically create custom test sets in any of the 581 languages supported by Wikidata and then to evaluate on them using the OddOneOut and Topk methods introduced in this paper.

For more details visit the github repository

Project details


Download files

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

Source Distribution

gensim_evaluations-0.1.1.tar.gz (7.8 kB view details)

Uploaded Source

Built Distribution

gensim_evaluations-0.1.1-py3-none-any.whl (15.8 kB view details)

Uploaded Python 3

File details

Details for the file gensim_evaluations-0.1.1.tar.gz.

File metadata

  • Download URL: gensim_evaluations-0.1.1.tar.gz
  • Upload date:
  • Size: 7.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.9.5

File hashes

Hashes for gensim_evaluations-0.1.1.tar.gz
Algorithm Hash digest
SHA256 91c02d30a8d9aee9fabc383b254df28262d59ea205d5c8abb6db905a713dc5ba
MD5 1a063f8eb597db975b1afd5b2239cbbd
BLAKE2b-256 c4389d990709b3648a9a401b9e1634b7362231d9d7889da6196bf3471b8d7678

See more details on using hashes here.

File details

Details for the file gensim_evaluations-0.1.1-py3-none-any.whl.

File metadata

  • Download URL: gensim_evaluations-0.1.1-py3-none-any.whl
  • Upload date:
  • Size: 15.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.9.5

File hashes

Hashes for gensim_evaluations-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 86da71832edc38ee533c444077cf8007c05b497d0e912543eaee995a6ede26f4
MD5 6ca0e8039d91303cb6d0e4cba4dbc06c
BLAKE2b-256 d20f1ce3786f0a7ece0f68d4d3db6c09d21751e6e740d3603928c44e1d7ed24c

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page