conceptnet-numberbatch-17-06-300

ConceptNet Numberbatch consists of state-of-the-art semantic vectors (also known as word embeddings) that can be used directly as a representation of word meanings or as a starting point for further machine learning.

Related issue #9.

attribute	value
File size	1.14GB
Number of vectors	1917247
Dimension	300
License	https://github.com/commonsense/conceptnet-numberbatch/blob/master/LICENSE.txt

Read more:

• http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14972
• https://github.com/commonsense/conceptnet-numberbatch
• http://conceptnet.io/

Example

import gensim.downloader as api

model = api.load("conceptnet-numberbatch-17-06-300")
for word, distance in model.most_similar("/c/en/beer"):
    print(u"{}: {:4f}".format(word, distance))

"""
output:

/c/ca/birra: 0.995633
/c/eu/zerbeza: 0.995058
/c/hi/बियर: 0.994754
/c/ja/ビア: 0.994656
/c/ja/ビヤ: 0.994406
/c/ja/ビーア: 0.994406
/c/eu/garagardo: 0.994178
/c/ku/بیرە: 0.993689
/c/eu/biera: 0.993634
/c/sh/пиво: 0.992218
"""