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==Word2vec== | ==Word2vec== | ||
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><br> | <code>pip install gensim</code><br> | ||
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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== | ||
Made by | Made by Facebook based on word2vec. Better at capturing syntactic relations (like apparent ---> apparently) see here: | ||
[https://rare-technologies.com/fasttext-and-gensim-word-embeddings/]<br> | [https://rare-technologies.com/fasttext-and-gensim-word-embeddings/]<br> | ||
Pretrained model files are HUGE | Pretrained model files are HUGE - this will be a problem on computers with less than 16GB Memory | ||
=== Installation + getting started: === | |||
Included in Gensim. Couldn't test yet due to memory constraints. | |||
Documentation is here: [https://radimrehurek.com/gensim/models/wrappers/fasttext.html] | |||
==GloVe== | ==GloVe== | ||
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". Seems to perform very slightly less well than Word2vec and FastWord. | |||
== pre trained models == | == pre trained models == | ||
* [https://github.com/Kyubyong/wordvectors https://github.com/Kyubyong/wordvectors: Word2Vec and FastText, Multiple languages, no english, trained on Wikipedia] | * [https://github.com/Kyubyong/wordvectors https://github.com/Kyubyong/wordvectors: Word2Vec and FastText, Multiple languages, no english, trained on Wikipedia] | ||
* [https://github.com/3Top/word2vec-api#where-to-get-a-pretrained-models https://github.com/3Top/word2vec-api Mostly GloVe, some word2vec, English, Trained on News, Wikipedia, Twitter] | * [https://github.com/3Top/word2vec-api#where-to-get-a-pretrained-models 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 https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md: Fasttext, all imaginable languages, trained on Wikipedia] | * [https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md: Fasttext, all imaginable languages, trained on Wikipedia] | ||
Revision as of 11:27, 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 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 Gensim. Couldn't test yet due to memory constraints. 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". Seems to perform very 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