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==GloVe== | ==GloVe== | ||
Invented by the Natural language processing group in standford | Invented by the Natural language processing group in standford [https://nlp.stanford.edu/projects/glove/]. Uses more conventional math instead of Neural Network "Black Magic" [https://www.quora.com/How-is-GloVe-different-from-word2vec]. Seems to perform just slightly less well than Word2vec and FastWord. | ||
== pre trained models == | == pre trained models == | ||
Revision as of 11:35, 8 May 2017
General Information on word embeddings
For a general explanation look here: [1]
Word2vec
Made by Google, uses Neural Net, performs good on semantics.
Installation + getting started:
Included in the gensim package.
To install, just type
pip install gensim
into a command window.
Here are some of the things you can do with the model: [2]
Here is a bit of background information an an explanation how to train your own models: [3].
Fastword
Made by Facebook based on word2vec. Better at capturing syntactic relations (like apparent ---> apparently) see here:
[4]
Pretrained model files are HUGE - this will be a problem on computers with less than 16GB Memory
Installation + getting started:
Included in the gensim package.
To install, just type
pip install gensim
into a command window.
Documentation is here: [5]
GloVe
Invented by the Natural language processing group in standford [6]. Uses more conventional math instead of Neural Network "Black Magic" [7]. Seems to perform just slightly less well than Word2vec and FastWord.
pre trained models
- https://github.com/Kyubyong/wordvectors: Word2Vec and FastText, Multiple languages, no english, trained on Wikipedia
- https://github.com/3Top/word2vec-api Mostly GloVe, some word2vec, English, Trained on News, Wikipedia, Twitter
- https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md: Fasttext, all imaginable languages, trained on Wikipedia