Understanding word vectors: A tutorial for "Reading and Writing Electronic Text," a class I teach at ITP. (Python 2.7) Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/

understanding-word-vectors.ipynb

Great, Thank You!

Great, well-explained tutorial, thank you!

Not sure why I'm getting the following error, working on macOS with Jupyter Lab, Python 2.7 and Spacy 2.0.9:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-2-090b6e832a74> in <module>()
      3 # It creates a list of unique words in the text
      4 tokens = list(set([w.text for w in doc if w.is_alpha]))
----> 5 print nlp.vocab['cheese'].vector

lexeme.pyx in spacy.lexeme.Lexeme.vector.__get__()

ValueError: Word vectors set to length 0. This may be because you don't have a model installed or loaded, or because your model doesn't include word vectors. For more info, see the documentation: 
https://spacy.io/usage/models

You want to download 'en_core_web_lg' model

OMG !! Really had a great time reading this beautiful gist. Very well explained.

Thanks!

I was led here by a tutorial on word vectors from youtube. Thanks for the simplicity!

very good

Thank you for sharing this. Excelent job!

this is amazing, thank you for explanation!!

Thanks!!

Very nice tutorial!

One question:
A word near the origin (0,0,0 ...) in the n-space has less possibility to be the result of an addition among words. As opposite, a word very distant of the origin could be the result of many possible additions among many words. Does this mean that complex concepts are far for the origin and basic concepts are near?