Classic Word Embeddings

Classic word embeddings are static and word-level, meaning that each distinct word gets exactly one pre-computed embedding. Most embeddings fall under this class, including the popular GloVe or Komninos embeddings.

Simply instantiate the WordEmbeddings class and pass a string identifier of the embedding you wish to load. So, if you want to use GloVe embeddings, pass the string 'glove' to the constructor:

from flair.embeddings import WordEmbeddings

# init embedding
glove_embedding = WordEmbeddings('glove')

Now, create an example sentence and call the embedding's embed() method. You can also pass a list of sentences to this method since some embedding types make use of batching to increase speed.

# create sentence.
sentence = Sentence('The grass is green .')

# embed a sentence using glove.
glove_embedding.embed(sentence)

# now check out the embedded tokens.
for token in sentence:
    print(token)
    print(token.embedding)

This prints out the tokens and their embeddings. GloVe embeddings are Pytorch vectors of dimensionality 100.

You choose which pre-trained embeddings you load by passing the appropriate id string to the constructor of the WordEmbeddings class. Typically, you use the two-letter language code to init an embedding, so 'en' for English and 'de' for German and so on. By default, this will initialize FastText embeddings trained over Wikipedia. You can also always use FastText embeddings over Web crawls, by instantiating with '-crawl'. So 'de-crawl' to use embeddings trained over German web crawls.

For English, we provide a few more options, so here you can choose between instantiating 'en-glove', 'en-extvec' and so on.

The following embeddings are currently supported:

ID	Language	Embedding
'en-glove' (or 'glove')	English	GloVe embeddings
'en-extvec' (or 'extvec')	English	Komninos embeddings
'en-crawl' (or 'crawl')	English	FastText embeddings over Web crawls
'en-twitter' (or 'twitter')	English	Twitter embeddings
'en-turian' (or 'turian')	English	Turian embeddings (small)
'en' (or 'en-news' or 'news')	English	FastText embeddings over news and wikipedia data
'de'	German	German FastText embeddings
'nl'	Dutch	Dutch FastText embeddings
'fr'	French	French FastText embeddings
'it'	Italian	Italian FastText embeddings
'es'	Spanish	Spanish FastText embeddings
'pt'	Portuguese	Portuguese FastText embeddings
'ro'	Romanian	Romanian FastText embeddings
'ca'	Catalan	Catalan FastText embeddings
'sv'	Swedish	Swedish FastText embeddings
'da'	Danish	Danish FastText embeddings
'no'	Norwegian	Norwegian FastText embeddings
'fi'	Finnish	Finnish FastText embeddings
'pl'	Polish	Polish FastText embeddings
'cz'	Czech	Czech FastText embeddings
'sk'	Slovak	Slovak FastText embeddings
'sl'	Slovenian	Slovenian FastText embeddings
'sr'	Serbian	Serbian FastText embeddings
'hr'	Croatian	Croatian FastText embeddings
'bg'	Bulgarian	Bulgarian FastText embeddings
'ru'	Russian	Russian FastText embeddings
'ar'	Arabic	Arabic FastText embeddings
'he'	Hebrew	Hebrew FastText embeddings
'tr'	Turkish	Turkish FastText embeddings
'fa'	Persian	Persian FastText embeddings
'ja'	Japanese	Japanese FastText embeddings
'ko'	Korean	Korean FastText embeddings
'zh'	Chinese	Chinese FastText embeddings
'hi'	Hindi	Hindi FastText embeddings
'id'	Indonesian	Indonesian FastText embeddings
'eu'	Basque	Basque FastText embeddings

So, if you want to load German FastText embeddings, instantiate as follows:

german_embedding = WordEmbeddings('de')

Alternatively, if you want to load German FastText embeddings trained over crawls, instantiate as follows:

german_embedding = WordEmbeddings('de-crawl')

We generally recommend the FastText embeddings, or GloVe if you want a smaller model.

If you want to use any other embeddings (not listed in the list above), you can load those by calling

custom_embedding = WordEmbeddings('path/to/your/custom/embeddings.gensim')

If you want to load custom embeddings you need to make sure that the custom embeddings are correctly formatted to gensim.

You can, for example, convert FastText embeddings to gensim using the following code snippet:

import gensim

word_vectors = gensim.models.KeyedVectors.load_word2vec_format('/path/to/fasttext/embeddings.txt', binary=False)
word_vectors.save('/path/to/converted')

However, FastText embeddings have the functionality of returning vectors for out of vocabulary words using the sub-word information. If you want to use this then try FastTextEmbeddings class.