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==Fastword== | ==Fastword== | ||
Made by Facebbok based on word2vec. Better at capturing syntactic relations (like apparent ---> apparently) see here: | Made by Facebbok based on word2vec. Better at capturing syntactic relations (like apparent ---> apparently) see here: | ||
[https://rare-technologies.com/fasttext-and-gensim-word-embeddings/] | [https://rare-technologies.com/fasttext-and-gensim-word-embeddings/]<br> | ||
Pretrained model files are HUGE | Pretrained model files are HUGE | ||
Revision as of 10:10, 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