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

Awesome good!

Vey intuitive 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

replace nlp.vocab['cheese'].vector with nlp('cheese').vector
and

def vec(s):
    return nlp.vocab[s].vector

with

def vec(s):
    return nlp(s).vector

very good explanation

Enjoyed reading this. Thank you!

One of the best tutorials on word to vec. Nevertheless there is a "quantum-leap" in the explanation when it comes to "Word vectors in spaCy". Suddenly we have vectors associated to any word, of a predetermined dimension. Why? Where are those vectors coming from? how are they calculated? Based on which texts? Since wordtovec takes into account context the vector representations are going to be very different in technical papers, in literature, poetry, facebook posts etc. How do you create your own vectors related to a particular collection of concepts over a particular set of documents? I observed this problematic in many many word2vec tutorials. The explanation starts very smoothly, basic, very well explained up to details; and suddenly there is a big hole in the explanation. In any case this is one of the best explanations I have found on wordtovec theory. thanks

I agree! I thought I had deleted many cells and downloaded it again looking for the gap.

When I ran snippets of code that access a library, it gave me errors like this: "FileNotFoundError: [Errno 2] No such file or directory: 'pg345.txt'". And same thing with the color file: "FileNotFoundError: [Errno 2] No such file or directory: 'xkcd.json'"
I ran those on jupyter notebook. Do you know what's wrong?

Note: I tried doing it in Visual Code but it gave me the same problem, even after saving it in the same directory. Also i've read online to use the absolute path, but it still would not work.

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?