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Made by Google, uses Neural Net, performs good on semantics. | Made by Google, uses Neural Net, performs good on semantics. | ||
=== Installation + getting started: === | === Installation + getting started: === | ||
<code>pip install gensim</code> | <code>pip install gensim</code><br> | ||
Here are some of the things you can do with the model: [http://textminingonline.com/getting-started-with-word2vec-and-glove-in-python] | Here are some of the things you can do with the model: [http://textminingonline.com/getting-started-with-word2vec-and-glove-in-python]<br> | ||
Here is a bit of background information an an explanation how to train your own models: [https://rare-technologies.com/word2vec-tutorial/]. | Here is a bit of background information an an explanation how to train your own models: [https://rare-technologies.com/word2vec-tutorial/]. | ||
==Fastword== | ==Fastword== | ||
Revision as of 10:09, 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:
pip install gensim
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 Facebbok based on word2vec. Better at capturing syntactic relations (like apparent ---> apparently) see here: [4] Pretrained model files are HUGE
GloVe
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