Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with gensim
Search options not deleted
user 35399
gensim is the python library for topic modelling. multi-dimensional vector representation of words or sentences which preserves semantic meaning is computed through word2vec and doc2vec models.
3
votes
2
answers
3k
views
When to use different Word2Vec training approaches?
I.e, Word2Vec in Tensorflow or Word2Vec trained with Gensim ?
In what cases would implementing it through the more manual first approach be useful vs. the second one? … If there is already an easier way to train a word2vec model using gensim, why is that not used always?
Furthermore, what is the benefit in using a pre-trained model like the Google News dataset? …
- 147